Do urban canyons influence street level grass pollen concentrations?

In epidemiological studies, outdoor exposure to pollen is typically estimated using rooftop monitoring station data, whilst exposure overwhelmingly occurs at street level. In this study the relationship between street level and roof level grass pollen concentrations was investigated for city centre street canyon environments in Aarhus, Denmark, and London, UK, during the grass pollen seasons of 2010 and 2011 respectively. For the period mid-day to late evening, street level concentrations in both cities tended to be lower than roof-level concentrations, though this difference was found to be statistically significant only in London. The ratio of street/roof level concentrations was compared with temperature, relative humidity, wind speed and direction, and solar radiation. Results indicated that the concentration ratio responds to wind direction with respect to relative canyon orientation and local source distribution. In the London study, an increase in relative humidity was linked to a significant decrease in street/roof level concentration ratio, and a possible causative mechanism involving moisture mediated pollen grain buoyancy is proposed. Relationships with the other weather variables were not found to be significant in either location. These results suggest a tendency for monitoring station data to overestimate exposure in the canyon environment.     

HDAC-mediated control of ERK- and PI3K-dependent TGF-β-induced extracellular matrix-regulating genes

Histone deacetylases (HDACs) regulate the acetylation of histones in the control of gene expression. Many non-histone proteins are also targeted for acetylation, including TGF-β signalling pathway components such as Smad2, Smad3 and Smad7. Our studies in mouse C3H10T1/2 fibroblasts suggested that a number of TGF-β-induced genes that regulate matrix turnover are selectively regulated by HDACs. Blockade of HDAC activity with trichostatin A (TSA) abrogated the induction of a disintegrin and metalloproteinase 12 (Adam12) and tissue inhibitor of metalloproteinases-1 (Timp-1) genes by TGF-β, whereas plasminogen activator inhibitor-1 (Pai-1) expression was unaffected. Analysis of the activation of cell signalling pathways demonstrated that TGF-β induced robust ERK and PI3K activation with delayed kinetics compared to the phosphorylation of Smads. The TGF-β induction of Adam12 and Timp-1 was dependent on such non-Smad signalling pathways and, importantly, HDAC inhibitors completely blocked their activation without affecting Smad signalling. Analysis of TGF-β-induced Adam12 and Timp-1 expression and ERK/PI3K signalling in the presence of semi-selective HDAC inhibitors valproic acid, MS-275 and apicidin implicated a role for class I HDACs. Furthermore, depletion of HDAC3 by RNA interference significantly down-regulated TGF-β-induced Adam12 and Timp-1 expression without modulating Pai-1 expression. Correlating with the effect of HDAC inhibitors, depletion of HDAC3 also blocked the activation of ERK and PI3K by TGF-β. Collectively, these data confirm that HDACs, and in particular HDAC3, are required for activation of the ERK and PI3K signalling pathways by TGF-β and for the subsequent gene induction dependent on these signalling pathways.     

Addressing PXR liabilities of phthalazine-based hedgehog/smoothened antagonists using novel pyridopyridazines

Pyridopyridazine antagonists of the hedgehog signaling pathway are described. Designed to optimize our previously described phthalazine smoothened antagonists, a representative compound eliminates a PXR liability while retaining potency and in vitro metabolic stability. Moreover, the compound has improved efficacy in a hedgehog/smoothened signaling mouse pharmacodynamic model.     

Regulation of the promoter region of the human adiponutrin/PNPLA3 gene by glucose and insulin

Previous biochemical, cardiovascular and behavioral work has given evidence for the existence of antagonistic galanin receptor-5-HT1A receptor interactions in the brain. In this study we investigated the existence of GalR1-5-HT1A receptor heteromers and their functional characteristics. In mammalian cells transfected with GFP2-tagged 5-HT1A receptor and YFP-tagged GalR1 receptor, a proximity-based fluorescence resonance energy transfer technique was used and it has been demonstrated that GalR1-5-HT1A receptors heteromerize. Furthermore, signaling by either the mitogen-activated protein kinase (MAPK) or adenylyl cyclase (AC) pathways by these heteromers indicates a trans-inhibition phenomenon through their interacting interface via allosteric mechanisms that block the development of an excessive activation of G_i/o and an exaggerated inhibition of AC or stimulation of MAPK activity. The presence of these heteromers in the discrete brain regions is postulated based on the existence of GalR-5-HT1A receptor-receptor interactions previously described in the brain and gives rise to explore possible novel therapeutic strategies for treatment of depression by targeting the GalR1-5-HT1A heteromers.     

Morphologically cryptic biological species within the liverwort Frullania asagrayana

? Premise of the study: The Frullania tamarisci complex includes eight Holarctic liverwort species. One of these, F. asagrayana, is distributed broadly throughout eastern North America from Canada to the Gulf Coast. Preliminary genetic data suggested that the species includes two groups of populations. This study was designed to test whether the two groups are reproductively isolated biological species. ? Methods: Eighty-eight samples from across the range of F. asagrayana, plus 73 samples from one population, were genotyped for 13 microsatellite loci. Sequences for two plastid loci and nrITS were obtained from 13 accessions. Genetic data were analyzed using coalescent models and Bayesian inference. ? Key results: Frullania asagrayana is sequence-invariant at the two plastid loci and ITS2, but two clear groups were resolved by microsatellites. The two groups are largely reproductively isolated, but there is a low level of gene flow from the southern to the northern group. No gene flow was detected in the other direction. A local population was heterogeneous but displayed strong genetic structure. ? Conclusions: The genetic structure of F. asagrayana in eastern North America reflects morphologically cryptic differentiation between reproductively isolated groups of populations, near-panmixis within groups, and clonal propagation at local scales. Reproductive isolation between groups that are invariant at the level of nucleotide sequences shows that caution must be exercised in making taxonomic and evolutionary inferences from reciprocal monophyly (or lack thereof) between putative species.     

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.     

Identification of Targeted Analyte Clusters for Studies of Schizophrenia

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.Schizophrenia is a debilitating neuropsychiatric disorder that affects more than 1% of the world population and costs hundreds of billions of United States dollars in healthcare provision and lost earnings (1). The diagnosis of this disease has not changed substantially over several decades and currently relies on subjective psychopathological ratings such as the Diagnostic and Statistical Manual of Mental Disorders (DSM)1-IV. Thus, diagnosis can be complicated by the presence of overlapping symptoms frequently occurring in other psychiatric illnesses such as bipolar disorder (BD) and major depressive disorder (MDD) and by the presence of confounding factors such as drug abuse and co-morbidities. This often results in diagnosis being delayed for several months to years. A delay in establishing an accurate diagnosis can have serious deleterious implications because a late or imprecise diagnosis can contribute to unsatisfactory outcomes to currently used drug therapies and to higher rates of relapse (2). Most importantly, more than half of schizophrenia subjects develop a progressive course of the disease associated with deficit symptoms (3).In contrast, early therapeutic intervention holds promise in preventing or diminishing such effects (4–6). An empirical assay for early and accurate diagnosis of schizophrenia would deliver improved patient outcomes and reduce the costs of the disease for healthcare services and society (7–9). Such an assay could also provide a means of stratifying patients and monitoring drug responses and may also lead to the development of translational medicine tools that are critical for discovery of novel therapeutic strategies. Molecular profiling methods that afford the simultaneous measurement of multiple analytes in clinical and preclinical samples have considerable promise in this endeavor. These methods have been aimed predominantly at identifying individual molecules that show differences in expression between the disease and control conditions. However, such studies have often been fraught with small fold-changes in analyte levels, a common obstacle when investigating complex neuropsychiatric disorders (10–12). Thus, standard statistical techniques such as t tests will not be able to explore patterned behaviors involving proteins that have subtle expression changes but still contribute to the development of schizophrenia.The main objective of this study was to determine whether unique patterns of biomarkers can be identified for subjects with first onset antipsychotic-naïve schizophrenia. Analyte expression lists were generated using the Multi-Analyte Profiling (MAP®) fluorescent bead-based technology for profiling serum samples from 77 male schizophrenia patients and 66 matched male controls. For comparison with other psychiatric disorders, we also analyzed the serum samples of 13 male BD and 17 male MDD patients. In parallel, serum samples from four relevant animal models were also profiled for comparison with the human disease state. Analysis of the respective expression lists was carried out using non-linear statistical analysis, which identifies small sets of analytes called targeted analyte clusters (TACs) that have the power to discriminate the patients from normal controls. We present here the performance of these clusters for diagnosis of schizophrenia. In addition, we show how this method can also contribute to increasing our understanding of the etiology of the disorder by determining its ability to classify various preclinical models of psychiatric disorders. The biological pathways associated with these clusters are discussed with their relevance to schizophrenia.