A publication in Amino Acids by researchers from UPF, CRG and other centers provides the first in vivo evidence of the involvement of the CHRNA5/A3/B4 gene cluster in nicotine addiction. It happens through modifying the activity of brain regions responsible for the balance between the rewarding and the aversive properties of this drug. CHRNA5/A3/B4 codes for the nicotinic acetylcholine receptor subunits A5, A3 and B4. Together they form the ligand-gated pentameric ion channels that modulate key physiological processes ranging from neurotransmission to cancer signaling. These receptors are activated by the neurotransmitter, acetylcholine, and the tobacco alkaloid, nicotine. Recently, the gene cluster received interest after a succession of linkage analyses and association studies identified multiple single-nucleotide polymorphisms in these genes that are associated with an increased risk for nicotine dependence and lung cancer.
To see the in vivo effects of the cluster, a transgenic mouse overexpressing the human CHRNA5/A3/B4 cluster was generated using a bacterial artificial chromosome. Transgenic mice showed increased functional receptors in brain regions where these subunits are highly expressed under normal physiological conditions. Moreover, they exhibited increased sensitivity to the pharmacological effects of nicotine. Transgenic mice also showed increased acquisition of nicotine self-administration.
Gallego X, Molas S, Amador-Arjona A, Marks MJ, Robles N, Murtra P, Armengol L, Fernández-Montes RD, Gratacòs M, Pumarola M, Cabrera R, Maldonado R, Sabrià J, Estivill X, Dierssen M. Overexpression of the CHRNA5/A3/B4 genomic cluster in mice increases the sensitivity to nicotine and modifies its reinforcing effects. Amino Acids. 2011 Nov 19
© 2010 Oncogene: Structure of the Nicotinic acetylcholine receptor (nAChR). (a) Schematic representation illustrating the pentameric arrangement of subunits in an assembled nAChR. (b) Conserved domains of a nAChR subunit including the amino (N) and carboxy (C) terminals, transmembrane segments (M1–M4) and the intracellular loop. (c) Assembly of heteromeric and homomeric nAChR subtypes. Individual nAChR subunits are represented as colored circles, with diamonds representing ligand-binding sites. Pentagons in the center of each pentamer represent the pore region.
The Human Pharmacology and Clinical Neurosciences group of the IMIM-Hospital del Mar, lead by Rafael de la Torre, has published a paper in PLoS One this week to try to clarify the association between cumulative use of MDMA (ecstasy), one of the most popular illegal psychostimulants abused among youth, and cognitive dysfunction. They have also set to understand the potential role of candidate genetic polymorphisms in explaining individual differences in the cognitive effects of MDMA.
Several studies have suggested that MDMA induces neurotoxicity, which primarily affects the serotonin system and is linked to memory dysfunction. There is also evidence that several gene polymorphisms may contribute to explain variations in the cognitive impact of MDMA across regular users of this drug.
The research group took 60 ecstasy polydrug users, 110 cannabis users and 93 non-drug users and assessed them using several cognitive measures. Participants were also genotyped for polymorphisms within six genes. The scientists found that both MDMA lifetime use and gene-related individual differences influence cognitive dysfunction in ecstasy users.
According to the authors “this study reliably demonstrates dose-related effects of MDMA use on visual attention, organization and memory”.
Cuyàs E, Verdejo-García A, Fagundo AB, Khymenets O, Rodríguez J, Cuenca A, de Sola Llopis S, Langohr K, Peña-Casanova J, Torrens M, Martín-Santos R, Farré M, de la Torre R. The Influence of Genetic and Environmental Factors among MDMA Users in Cognitive Performance. PLoS One. 2011;6(11):e27206 [PDF]
Very interesting talk by Edward Marcotte today at the PRBB!
He is an expert in proteomics, but touches all aspects of systems biology, and today he asked the following question: how does genotype determine phenotype? Can we predict the outcome of all the genomic variation we are uncovering with the many genomic projects we are doing nowadays?
Well, his lab is certainly trying to do so, and using three different strategies which I will summarise very briefly:
1. Using functional gene networks, which are based on data such as mRNA expression, protein-protein interactions (PPI), etc. These networks presumably are formed by genes that are involved in the same biological processes. From here one only needs to follow the “guilt-by-assotiation” principle and assume that, if a gene in that network is involved in a particular phenotype (a disease, for example), the genes around it might also be so. They have tested this in yeast, C.elegans, Arabidopsis, rice and mouse, at least. They have managed to validate predictions for up to 200 genes. And they have come up with a valuable principle: that phenotypes reflect biological modules, rather than single proteins. That is, it is not a specific proteins that is essential, but a specific complex.
2. A systematic mapping of stable protein complexes in humans, which they have done in collaboration with labs in Toronto and which includes more than 2000 Mass spec experiments. From here they have inferred more than 600 stable complexes in humans (more than 500 of them with more than 3 components), of which 1/3 are unknown. Now the idea is to use this PPI network as a framework for linking genes to diseases. And they are doing so with one children developmental disease, the Cornelia de Lange syndrome, for which 3 known genes explain only the 50% of cases. They have selected some of the proteins which are around those three in the network and are currently sequencing them in patients.
3. Using model organisms to infer human disease genes. This is by far the one that I was most surprised about. It turns out that looking for what he called phenologs (orthologous phenotypes between organisms, for example, which yeast phenotype is equivalent to breast cancer in humans) one can find surprising disease models. For example, yeast sensitivity to lovastatin is a model for angiogenesis defects in humans! This is found looking for the yeast orthologous of human genes involved in angiogenesis, and checking which phenotype those yeast genes are involved in. Then one can look at the rest of the yeast genes involved in that phenotype, and check if their human orthologous might be involved in angiogenesis.
And then, in principle, one could even use screening in yeast to find angiogenesis inhibitors. And the surprising thing is that it works! The Marcotte lab is actually about to start a phase I clinical trial on a drug they found this way and which they hope might be useful for glioblastoma. This is just one example, but according to him, this ‘phenologs’ strategy seems to work for more than 50% of the human genetic diseases… One big lesson that stems from this knowledge is that protein modules are conserved through evolution even if the phenotype is not – a concept he called ‘evolutionary repurposing’. Very interesting indeed.
Report by Maruxa Martinez, Scientific Editor at the PRBB
An interview published in Ellipse, the monthly magazine of the PRBB.
Audrey de Nazelle arrived here three years ago from the University of North Carolina Chapel Hill where she had done a PhD in Environmental Science. This French mathematician, an ecological activist from a young age, has always understood the importance of politics in public health.
She is currently doing a postdoc in Mark Nieuwenhuijsen’s group at CREAL, and together with him is leading the TAPAS project, which started in 2009 and which will run until 2012.
Who is participating in TAPAS and what is its aim?
We are the coordinators, but there are research groups from six European countries involved. We want to understand the health impact of policies on active transport, such as walking or cycling, and we are using quantitative models to measure this impact in six cities: Barcelona, Basel, Copenhagen, Paris, Prague, and Warsaw. To do this we use both the existing literature and local data.
What are these models for?
The idea is that one can choose a specific policy of active transport, for example the introduction of a network of bike lanes or a congestion charge such as there is in London, and predict the effects on health that come about because of this. We hope that the models will serve as a tool to assist policy makers with their decisions.
What kind of effects are we talking about?
Basically, an increase in active transport means an increase in the physical activity of the population. In addition, a reduction of motorised modes of transport means less gases emitted and therefore an improvement in air quality and climate change mitigation. But our studies do not ignore the possible adverse effects such as an increase in accidents or a greater inhalation of contaminants by those who walk or cycle.
What studies have you done so far?
We made a first theoretical model of the “Bicing” system and undertook experimental studies that allowed us to increase our knowledge. These included a survey of 800 people to understand their behaviour with respect to active transport. We supplemented the survey with a pilot study of 35 volunteers to obtain more objective measurements. For five days they carried around devices which gathered data about their location with a GPS, as well as their level of physical activity using a piece of built-in smartphone software.
What is this kind of information useful for?
We can, for example, analyse the types of routes that people use and what their choice of means of transport depends on. We can also compare their routes with pollution maps and analyse their exposure.
So smartphones are the future of exposure analysis?
I think so, yes. Both this technology and other developments, such as remote sensing, which are part of what is known as “ubiquitious sensing”. As far as we know, we were the first people to use this smartphone technology in a real study with volunteers. But if we think that there are billions of people who use mobile phones, and that they could all become data collection volunteers, the potential is enormous. In addition to what we have already done we could add noise or pollution sensors, for example, with everything pinpointed to its exact location.
Chromosome segregation requires the formation of K-fibres, microtubule bundles that attach sister kinetochores to spindle poles. Most K-fibre microtubules originate around the chromosomes through a non-centrosomal RanGTP-dependent pathway and become oriented with the plus ends attached to the kinetochore and the minus ends focused at the spindle poles. The capture and stabilization of microtubule plus ends at the kinetochore has been extensively studied but very little is known on how their minus-end dynamics are controlled.
Here Isabelle Verno’s lab at the CRG shows that MCRS1 is a RanGTP-regulated factor essential for non-centrosomal microtubule assembly. MCRS1 localizes to the minus ends of chromosomal microtubules and K-fibres, where it protects them from depolymerization. Their data reveal the existence of a mechanism that stabilizes the minus ends of chromosomal microtubules and K-fibres, and is essential for the assembly of a functional bipolar spindle.
Meunier S, Vernos I. K-fibre minus ends are stabilized by a RanGTP-dependent mechanism essential for functional spindle assembly. Nat Cell Biol. 2011 Nov 13;