Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions.

Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, ie, it is doubly robust, and can yield valid inference when used in conjunction with machine learning. It also has the advantage of easy-to-calculate analytic standard errors in contrast to the g-formula, which requires bootstrapping. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings, and we compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies: (1) long follow-up time (30 months); (2) gradually declining sample size; (3) limited support for some intervention rules of interest; (4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods; and (5) consideration of collider bias. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: We show that (1) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (2) machine learning utilized with a small set of simple learners (if more complex ones cannot be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; and (3) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis. We provide guidance for the practical application of LTMLE

A Meta-analysis of Attachment to Parents and Delinquency

To investigate the link between attachment to parents and delinquency, and the potential moderating effects of age and sex, 74 published and unpublished manuscripts (N = 55,537 participants) were subjected to a multilevel meta-analysis. A mean small to moderate effect size was found (r = 0.18). Poor attachment to parents was significantly linked to delinquency in boys and girls. Stronger effect sizes were found for attachment to mothers than attachment to fathers. In addition, stronger effect sizes were found if the child and the parent had the same sex compared to cross-sex pairs of children and parents. Age of the participants moderated the link between attachment and delinquency: larger effect sizes were found in younger than in older participants. It can be concluded that attachment is associated with juvenile delinquency. Attachment could therefore be a target for intervention to reduce or prevent future delinquent behavior in juveniles

Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men

Background: Previous studies indicate multiple influences on the overall health of HIV-infected persons; however, few assess and rank longitudinal changes in social and structural barriers that are disproportionately found in impoverished populations. We empirically ranked factors that longitudinally impact the overall health status of HIV-infected homeless and unstably housed men. Methods and Findings: Between 2002 and 2008, a cohort of 288 HIV+ homeless and unstably housed men was recruited and followed over time. The population was 60 % non-Caucasian and the median age was 41 years; 67 % of study participants reported recent drug use and 20 % reported recent homelessness. At baseline, the median CD4 cell count was 349 cells/ml and 18 % of eligible persons (CD4,350) took antiretroviral therapy (ART). Marginal structural models were used to estimate the population-level effects of behavioral, social, and structural factors on overall physical and mental health status (measured by the SF-36), and targeted variable importance (tVIM) was used to empirically rank factors by their influence. After adjusting for confounding, and in order of their influence, the three factors with the strongest negative effects on physical health were unmet subsistence needs, Caucasian race, and no reported source of instrumental support. The three factors with the strongest negative effects on mental health were unmet subsistence needs, not having a close friend/confidant, and drug use. ART adherence.90 % ranked 5th for its positive influence on mental health, and viral loa

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity

The 5-HT2C receptor agonist meta-chlorophenylpiperazine (mCPP) reduces palatable food consumption and BOLD fMRI responses to food images in healthy female volunteers

RATIONALE: Brain 5-HT2C receptors form part of a neural network that controls eating behaviour. 5-HT2C receptor agonists decrease food intake by activating proopiomelanocortin (POMC) neurons in the arcuate nucleus of the hypothalamus, but recent research in rodents has suggested that 5-HT2C receptor agonists may also act via dopaminergic circuitry to reduce the rewarding value of food and other reinforcers. No mechanistic studies on the effects of 5-HT2C agonists on food intake in humans have been conducted to date. OBJECTIVES: The present study examined the effects of the 5-HT2C receptor agonist meta-chlorophenylpiperazine (mCPP) on food consumption, eating microstructure and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) responses to food pictures in healthy female volunteers. METHODS: In a double-blind, placebo-controlled, crossover design, participants were randomized immediately after screening to receive oral mCPP (30mg) in a single morning dose, or placebo, in a counterbalanced order. Test foods were served from a Universal Eating Monitor (UEM) that measured eating rate and fMRI BOLD signals to the sight of food and non-food images were recorded. RESULTS: mCPP decreased rated appetite and intake of a palatable snack eaten in the absence of hunger but had no significant effect on the consumption of a pasta lunch (although pasta eating rate was reduced). mCPP also decreased BOLD fMRI responses to the sight of food pictures in areas of reward-associated circuitry. A post hoc analysis identified individual variability in the response to mCPP (exploratory responder-non-responder analysis). Some participants did not reduce their cookie intake after treatment with mCPP and this lack of response was associated with enhanced ratings of cookie pleasantness and enhanced baseline BOLD responses to food images in key reward and appetite circuitry. CONCLUSIONS: These results suggest that 5-HT2C receptor activation in humans inhibits food reward-related responding and that further investigation of stratification of responding to mCPP and other 5-HT2C receptor agonists is warranted

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p

Female responses to experimental removal of sexual selection components in Drosophila melanogaster

Despite the common assumption that multiple mating should in general be favored in males, but not in females, to date there is no consensus on the general impact of multiple mating on female fitness. Notably, very little is known about the genetic and physiological features underlying the female response to sexual selection pressures. By combining an experimental evolution approach with genomic techniques, we investigated the effects of single and multiple matings on female fecundity and gene expression. We experimentally manipulated the opportunity for mating in replicate populations of Drosophila melanogaster by removing components of sexual selection, with the aim of testing differences in short term post-mating effects of females evolved under different mating strategies

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection.

The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities

Violent aggression predicted by multiple pre-adult environmental hits

Early exposure to negative environmental impact shapes individual behavior and potentially contributes to any mental disease. We reported previously that accumulated environmental risk markedly decreases age at schizophrenia onset. Follow-up of matched extreme group individuals (≤1 vs. ≥3 risks) unexpectedly revealed that high-risk subjects had >5 times greater probability of forensic hospitalization. In line with longstanding sociological theories, we hypothesized that risk accumulation before adulthood induces violent aggression and criminal conduct, independent of mental illness. We determined in 6 independent cohorts (4 schizophrenia and 2 general population samples) pre-adult risk exposure, comprising urbanicity, migration, physical and sexual abuse as primary, and cannabis or alcohol as secondary hits. All single hits by themselves were marginally associated with higher violent aggression. Most strikingly, however, their accumulation strongly predicted violent aggression (odds ratio 10.5). An epigenome-wide association scan to detect differential methylation of blood-derived DNA of selected extreme group individuals yielded overall negative results. Conversely, determination in peripheral blood mononuclear cells of histone-deacetylase1 mRNA as 'umbrella mediator' of epigenetic processes revealed an increase in the high-risk group, suggesting lasting epigenetic alterations. Together, we provide sound evidence of a disease-independent unfortunate relationship between well-defined pre-adult environmental hits and violent aggression, calling for more efficient prevention