Long Lasting Local and Systemic Inflammation after Cerebral Hypoxic ischemia in Newborn Mice

Background: Hypoxic ischemia (HI) is an important cause of neonatal brain injury and subsequent inflammation affects neurological outcome. In this study we performed investigations of systemic and local activation states of inflammatory cells from innate and adaptive immunity at different time points after neonatal HI brain injury in mice. Methodology/Principal Findings: We developed a multiplex flow cytometry based method combined with immunohistochemistry to investigate cellular immune responses in the brain 24 h to 7 months after HI brain injury. In addition, functional studies of ex vivo splenocytes after cerebral hypoxic ischemia were performed. Both central and peripheral activation of CD11b + and CD11c + antigen presenting cells were seen with expression of the costimulatory molecule CD86 and MHC-II, indicating active antigen presentation in the damaged hemisphere and in the spleen. After one week, naïve CD45rb + T-lymphocytes were demonstrated in the damaged brain hemisphere. In a second phase after three months, pronounced activation of CD45rb 2 T-lymphocytes expressing CD69 and CD25 was seen in the damaged hemisphere. Brain homogenate induced proliferation in splenocytes after HI but not in controls. Conclusions/Significance: Our findings demonstrate activation of both local and systemic immune responses months after hypoxic ischemic neonatal brain injury. The long term immune activation observed is of general importance for future studies of the inflammatory response after brain injury as most previous studies have focused on the first few weeks afte

Genetic contributions to self-reported tiredness

Self-reported tiredness and low energy, often called fatigue, are associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6 and 50%. In the UK Biobank sample (N=108 976), we carried out a genome-wide association study (GWAS) of responses to the question, ‘Over the last two weeks, how often have you felt tired or had little energy?’ Univariate GCTA-GREML found that the proportion of variance explained by all common single-nucleotide polymorphisms for this tiredness question was 8.4% (s.e.=0.6%). GWAS identified one genome-wide significant hit (Affymetrix id 1:64178756_C_T; P=1.36 × 10−11). Linkage disequilibrium score regression and polygenic profile score analyses were used to test for shared genetic aetiology between tiredness and up to 29 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and body mass index (BMI), C-reactive protein, high-density lipoprotein (HDL) cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, attention deficit hyperactivity disorder, bipolar disorder, major depressive disorder, neuroticism, schizophrenia and verbal-numerical reasoning (absolute rg effect sizes between 0.02 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, low-density lipoprotein cholesterol, coronary artery disease, C-reactive protein, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder and schizophrenia (standardised β’s had absolute values<0.03). These results suggest that tiredness is a partly heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality and physiological processes

Epigenetic differences in monozygotic twins discordant for major depressive disorder

Although monozygotic (MZ) twins share the majority of their genetic makeup, they can be phenotypically discordant on several traits and diseases. DNA methylation is an epigenetic mechanism that can be influenced by genetic, environmental and stochastic events and may have an important impact on individual variability. In this study we explored epigenetic differences in peripheral blood samples in three MZ twin studies on major depressive disorder (MDD). Epigenetic data for twin pairs were collected as part of a previous study using 8.1-K-CpG microarrays tagging DNA modification in white blood cells from MZ twins discordant for MDD. Data originated from three geographical regions: UK, Australia and the Netherlands. Ninety-seven MZ pairs (194 individuals) discordant for MDD were included. Different methods to address non independently-and-identically distributed (non-i.i.d.) data were evaluated. Machine-learning methods with feature selection centered on support vector machine and random forest were used to build a classifier to predict cases and controls based on epivariations. The most informative variants were mapped to genes and carried forward for network analysis. A mixture approach using principal component analysis (PCA) and Bayes methods allowed to combine the three studies and to leverage the increased predictive power provided by the larger sample. A machine-learning algorithm with feature reduction classified affected from non-affected twins above chance levels in an independent training-testing design. Network analysis revealed gene networks centered on the PPAR-γ (NR1C3) and C-MYC gene hubs interacting through the AP-1 (c-Jun) transcription factor. PPAR-γ (NR1C3) is a drug target for pioglitazone, which has been shown to reduce depression symptoms in patients with MDD. Using a data-driven approach we were able to overcome challenges of non-i.i.d. data when combining epigenetic studies from MZ twins discordant for MDD. Individually, the studies yielded negative results but when combined classification of the disease state from blood epigenome alone was possible. Network analysis revealed genes and gene networks that support the inflammation hypothesis of MDD

Comparison of clinical and parasitological data from controlled human malaria infection trials.

Contains fulltext : 110636.pdf (publisher's version ) (Open Access)BACKGROUND: Exposing healthy human volunteers to Plasmodium falciparum-infected mosquitoes is an accepted tool to evaluate preliminary efficacy of malaria vaccines. To accommodate the demand of the malaria vaccine pipeline, controlled infections are carried out in an increasing number of centers worldwide. We assessed their safety and reproducibility. METHODS: We reviewed safety and parasitological data from 128 malaria-naive subjects participating in controlled malaria infection trials conducted at the University of Oxford, UK, and the Radboud University Nijmegen Medical Center, The Netherlands. Results were compared to a report from the US Military Malaria Vaccine Program. RESULTS: We show that controlled human malaria infection trials are safe and demonstrate a consistent safety profile with minor differences in the frequencies of arthralgia, fatigue, chills and fever between institutions. But prepatent periods show significant variation. Detailed analysis of Q-PCR data reveals highly synchronous blood stage parasite growth and multiplication rates. CONCLUSIONS: Procedural differences can lead to some variation in safety profile and parasite kinetics between institutions. Further harmonization and standardization of protocols will be useful for wider adoption of these cost-effective small-scale efficacy trials. Nevertheless, parasite growth rates are highly reproducible, illustrating the robustness of controlled infections as a valid tool for malaria vaccine development