Cerebral aspergillosis simulating pyogenic abscesses

Background: A 29-year-young male patient with a history of HIV for approximately 4 years ago was admitted to the department of Internal Medicine for fever, with a headache resistant to analgesics. He also presented nausea without vomiting, and the fever persisted despite antibiotic treatment. CT scan was carried out, followed by MRI in order to better characterize the lesions

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/

Maps of Open Chromatin Guide the Functional Follow-Up of Genome-Wide Association Signals: Application to Hematological Traits

Turning genetic discoveries identified in genome-wide association (GWA) studies into biological mechanisms is an important challenge in human genetics. Many GWA signals map outside exons, suggesting that the associated variants may lie within regulatory regions. We applied the formaldehyde-assisted isolation of regulatory elements (FAIRE) method in a megakaryocytic and an erythroblastoid cell line to map active regulatory elements at known loci associated with hematological quantitative traits, coronary artery disease, and myocardial infarction. We showed that the two cell types exhibit distinct patterns of open chromatin and that cell-specific open chromatin can guide the finding of functional variants. We identified an open chromatin region at chromosome 7q22.3 in megakaryocytes but not erythroblasts, which harbors the common non-coding sequence variant rs342293 known to be associated with platelet volume and function. Resequencing of this open chromatin region in 643 individuals provided strong evidence that rs342293 is the only putative causative variant in this region. We demonstrated that the C- and G-alleles differentially bind the transcription factor EVI1 affecting PIK3CG gene expression in platelets and macrophages. A protein–protein interaction network including up- and down-regulated genes in Pik3cg knockout mice indicated that PIK3CG is associated with gene pathways with an established role in platelet membrane biogenesis and thrombus formation. Thus, rs342293 is the functional common variant at this locus; to the best of our knowledge this is the first such variant to be elucidated among the known platelet quantitative trait loci (QTLs). Our data suggested a molecular mechanism by which a non-coding GWA index SNP modulates platelet phenotype

The Predisposition for Type 2 Diabetes Mellitus and Metabolic Syndrome

Type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) are diseases caused by the interaction of genetic and non-genetic factors. Therefore, the aim of our study was to investigate the association between six common genetic polymorphisms and T2DM and MetS in males. A total of 120 T2DM, 75 MetS, and 120 healthy controls (HC) were included in the study. ACE ID, eNOS 4a/b, ATR1 A1166C, OXTR (A>G), SOD1 +35A/C, CAT-21A/T gene polymorphisms were genotyped by PCR or PCR-RFLP techniques. T2DM was diagnosed at an earlier age compared to MetS (54 vs 55 years old, p=0.0003) and the difference was greater in carriers of the OXTR G allele (54 vs 56 years old, p=0.0002) or both OXTR G and eNOS b alleles (54 vs 56, p=0.00016). The SOD1 AA genotype (O.R.=0.11, p=0.0006) and the presence of both ACE I and OXTR1 A (O.R.=0.39, p=0.0005) alleles revealed to be protective for T2DM. SOD1 AA and AC genotypes were protective factors for triglyceride (p=0.0002 and p=0.0005, respectively) and HDL cholesterol (p=0.0002 and p=0.0004, respectively) levels in T2DM patients. ACE DD was identified more frequently in hypertensive T2DM patients (O.R.=3.77, p=0.0005) and in those who reported drinking alcohol (p=0.0001) comparing to HC and T2DM patients who did not drink alcohol, respectively. We observed that T2DM patients who reported drinking alcohol had an increased frequency of ACE DD and eNOS bb (p<0.0001), or ACE DD and OXTR G (p<0.0001) compared to non-drinkers. No gene polymorphisms were associated with MetS

Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes.

Genome-wide association studies have identified many genetic variants associated with complex traits. However, at only a minority of loci have the molecular mechanisms mediating these associations been characterized. In parallel, whereas cis regulatory patterns of gene expression have been extensively explored, the identification of trans regulatory effects in humans has attracted less attention. Here we show that the type 2 diabetes and high-density lipoprotein cholesterol-associated cis-acting expression quantitative trait locus (eQTL) of the maternally expressed transcription factor KLF14 acts as a master trans regulator of adipose gene expression. Expression levels of genes regulated by this trans-eQTL are highly correlated with concurrently measured metabolic traits, and a subset of the trans-regulated genes harbor variants directly associated with metabolic phenotypes. This trans-eQTL network provides a mechanistic understanding of the effect of the KLF14 locus on metabolic disease risk and offers a potential model for other complex traits

Gene expression changes with age in skin, adipose tissue, blood and brain.

BACKGROUND: Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age. RESULTS: Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues. CONCLUSIONS: Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases