The regulation of monoamine oxidase: a gene expression by distinct variable number tandem repeats

The monoamine oxidase A (MAOA) uVNTR (upstream variable number tandem repeat) is one of the most often cited examples of a gene by environment interaction (GxE) in relation to behavioral traits. However, MAOA possesses a second VNTR, 500 bp upstream of the uVNTR, which is termed d- or distal VNTR. Furthermore, genomic analysis indicates that there are a minimum of two transcriptional start sites (TSSs) for MAOA, one of which encompasses the uVNTR within the 5′ untranslated region of one of the isoforms. Through expression analysis in semi-haploid HAP1 cell lines genetically engineered in order to knockout (KO) either the uVNTR, dVNTR, or both VNTRs, we assessed the effect of the two MAOA VNTRs, either alone or in combination, on gene expression directed from the different TSSs. Complementing our functional analysis, we determined the haplotype variation of these VNTRs in the general population. The expression of the two MAOA isoforms was differentially modulated by the two VNTRs located in the promoter region. The most extensively studied uVNTR, previously considered a positive regulator of the MAOA gene, did not modulate the expression of what it is considered the canonical isoform, while we found that the dVNTR positively regulated this isoform in our model. In contrast, both the uVNTR and the dVNTR were found to act as negative regulators of the second less abundant MAOA isoform. The haplotype analysis for these two VNTRs demonstrated a bias against the presence of one of the potential variants. The uVNTR and dVNTR differentially affect expression of distinct MAOA isoforms, and thus, their combined profiling offers new insights into gene-regulation, GxE interaction, and ultimately MAOA-driven behavior

Involvement of microRNA Lethal-7a in the Regulation of Embryo Implantation in Mice

MicroRNAs interact with multiple mRNAs resulting in their degradation and/or translational repression. This report used the delayed implantation model to determine the role of miRNAs in blastocysts. Dormant blastocysts in delayed implanting mice were activated by estradiol. Differential expression of 45 out of 238 miRNAs examined was found between the dormant and the activated blastocysts. Five of the nine members of the microRNA lethal-7 (let-7) family were down-regulated after activation. Human blastocysts also had a low expression of let-7 family. Forced-expression of a family member, let-7a in mouse blastocysts decreased the number of implantation sites (let-7a: 1.1±0.4; control: 3.8±0.4) in vivo, and reduced the percentages of blastocyst that attached (let-7a: 42.0±8.3%; control: 79.0±5.1%) and spreaded (let-7a: 33.5±2.9%; control: 67.3±3.8%) on fibronectin in vitro. Integrin-β3, a known implantation-related molecule, was demonstrated to be a target of let-7a by 3′-untranslated region reporter assay in cervical cancer cells HeLa, and Western blotting in mouse blastocysts. The inhibitory effect of forced-expression of let-7a on blastocyst attachment and outgrowth was partially nullified in vitro and in vivo by forced-expression of integrin-β3. This study provides the first direct evidence that let-7a is involved in regulating the implantation process partly via modulation of the expression of integrin-β3. (200 words)

Fifth European Dirofilaria and Angiostrongylus Days (FiEDAD) 2016

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A Bayesian approach to fitting Gibbs processes with temporal random effects

This work is partially supported by Research Councils UKWe consider spatial point pattern data that have been observed repeatedly over a period of time in an inhomogeneous environment. Each spatial point pattern can be regarded as a “snapshot” of the underlying point process at a series of times. Thus, the number of points and corresponding locations of points differ for each snapshot. Each snapshot can be analyzed independently, but in many cases there may be little information in the data relating to model parameters, particularly parameters relating to the interaction between points. Thus, we develop an integrated approach, simultaneously analyzing all snapshots within a single robust and consistent analysis. We assume that sufficient time has passed between observation dates so that the spatial point patterns can be regarded as independent replicates, given spatial covariates. We develop a joint mixed effects Gibbs point process model for the replicates of spatial point patterns by considering environmental covariates in the analysis as fixed effects, to model the heterogeneous environment, with a random effects (or hierarchical) component to account for the different observation days for the intensity function. We demonstrate how the model can be fitted within a Bayesian framework using an auxiliary variable approach to deal with the issue of the random effects component. We apply the methods to a data set of musk oxen herds and demonstrate the increased precision of the parameter estimates when considering all available data within a single integrated analysis.PostprintPeer reviewe