“You went to high school and you learned genetics. You heard about a certain Gregor Mendel who crossed peas and came up with the idea that there is a dominant and a recessive allele. You did not particularly like the guy because there would always be a question about peas with recessive and dominant alleles at the exam. But you grew up, became wiser and just as you started to like him, you heard from someone that he faked his data….”
Did he or didn’t he?
You can read Guillaume Filion’s latest blog entry about the father of Mendelian genetics and statistics – but you will have to choose by yourself!
Methadone maintenance treatment (MMT) is the most widely-used therapy in opioid dependence, but it is not effective in some patients, who relapse or drop out from treatment. Researchers at the IMIM and Hospital del Mar led by Marta Torrens, in collaboration with colleagues at the CRG, have found a possible explanation of why some people may not respond well to this treatment.
As the authors explain in their paper published this month in the journal European Neuropsychopharmacology, they carried out a genetic analysis on several patients, focusing on the gene ALDH5A1. This enzyme is involved in the catabolism of the neurotransmitter gamma-aminobutyric acid (GABA), the major inhibitory neurotransmitter in the mammalian central nervous system. ALDH5A1 comes in many forms, and the scientists found that subjects carrying the T variant allele had a higher risk to be nonresponders to methadone treatment. They hypothesized that this could be due to a reduction in the ALDH5A1 enzyme activity, which would increase endogenous GABA levels and therefore induce symptoms such as sedation and impaired psychomotor performance. These neuropsychological effects related with the reduction in enzyme activity could be responsible for a higher propensity to relapse in these genetically predisposed patients.
The findings could be helpful to predict which subjects with opioid dependence problems would probably not benefit from methadone maintenance treatment and could use other treatments instead, such as diamorphine.
Fonseca F, Gratacòs M, Escaramís G, De Cid R, Martín-Santos R, Farré M, Estivill X, Torrens M. ALDH5A1 variability in opioid dependent patients could influence response to methadone treatment. Eur Neuropsychopharmacol. 2013 Oct 18;
Genome-wide association studies (GWAS) have revolutionized the field of complex disease genetics in the last six years. Many disease associations (i.e. genetic variants that increase risk for a specific disease) have been detected using this technique, but the reported variants tend to explain only small fractions of risk. Also, the causal variants that generate the associations unveiled by GWAS have not been identified. And their frequency and degree of sharing across different ethnical populations remains unknown.
Arcadi Navarro, from the Institute of Evolutionary Biology (UPF-CSIC), set out to study the degree of sharing of disease-associated variants across populations, in order to help solving these issues. Together with Urko Marigorta, they did a comprehensive survey of GWAS replicability across 28 diseases. As they report in their paper in PLOS Genetics, most loci and SNPs discovered in Europeans for these conditions had been extensively replicated using peoples of European and East Asian ancestry, while replication with individuals of African ancestry proved to be much less common.
The authors found a strong and significant correlation across Europeans and East Asians, indicating that underlying causal variants are common and shared between the two ancestries and that they tend to map close to the associated marker SNPs.
They also observed that GWAS with larger sample sizes have detected variants with weaker effects but not with lower frequencies. This indicates that most GWAS results are due to common variants.
Marigorta UM, Navarro A. High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants. PLoS Genet. 2013 Jun;9(6):e1003566
In a recent work published in Neurobiology of disease, the research groups lead by Mara Dierssen at the CRG and Cristina Fillat, now at the August Pi i Sunyer Biomedical Research Institute (IDIBAPS) have joined efforts to find a potential therapeutic target for Down Syndrome (mice).
Down Syndrome (DS), caused by the trisomy of human chromosome 21 (HSA21), is the most common chromosome abnormality in humans. It is typically associated with a delay in cognitive ability, with an average IQ of around 50 in young adults compared with 100 in adults without the condition, as well as with physical growth and a particular set of facial characteristics.
The Ts65Dn mouse is a genetic model for DS with a trisomy of the homologous chromosome in mice. In this model, overexpression of HSA21 homologous genes has been associated with strong visuo-spatial cognitive alterations, ascribed to hippocampal dysfunction. One of these genes is Dyrk1A (Dual specificity tyrosine-phosphorylation-regulated kinase 1A), a candidate gene for DS which seems to play a significant role in a signaling pathway regulating cell proliferation and which may be involved in brain development.
Dierssen, Fillat and colleagues decided to study whether the normalization of the expression levels of Dyrk1A – that is, reducing its expression, which in these mice is double than normal – might correct hippocampal defects in Ts65Dn mice.
They injected adeno-associated viruses containing a short hairpin RNA against Dyrk1A and a Luciferase reporter gene in the hippocampus of 2 months-old Ts65Dn mice. After checking that the injected hippocampi were efficiently transduced (via bioluminescence in vivo imaging, luciferase activity quantification and immunohistochemical analysis) and that the Dyrk1A expression had indeed been normalized at the molecular level, the researchers compared electrophysiological recordings of hippocampal slices from the Ts65Dn injected mice with those from mice injected with an AAV2/1 control virus.
The mice with normalized Dyrk1A levels displayed attenuation of the synaptic plasticity defects of trisomic mice. They also showed partial improvement in their hippocampal-dependent search strategy, as seen in the Morris water maze task – although long-term consolidation of the task was not achieved.
As the authors conclude, these results show Dyrk1A as a critical player in the pathophysiology of DS and define Dyrk1A as a therapeutic target in adult trisomic mice.
Altafaj X, Martín E, Ortiz-Abalia J, Valderrama A, Lao-Peregrin C, Dierssen M, Fillat C. Normalization of Dyrk1A expression by AAV2/1-shDyrk1A attenuates hippocampal-dependent defects in the Ts65Dn mouse model of Down syndrome. Neurobiol Dis. 2012 Dec 4;
The English researcher Ben Lehner started as a junior group leader at the CRG in December 2006 and has been an ICREA Professor since 2009. His lab, Genetics Systems, consists of five postdoctoral fellows, four PhD students, and a technician who hail from Italy, the UK, Germany, the Netherlands, Poland, Chile, Peru, Canada and Switzerland. About half of the group members are computational biologists and the rest work primarily in the ‘wet’ lab. They all have the same aim – to understand basic questions in genetics – but they use diverse approaches and model systems.
From individual genome sequences to individual phenotypes
“In humans many mutations in genes are associated with an increased risk of particular diseases such as cancer”, the scientist explains. “But human geneticists are terrible at predicting disease risk. Most people with disease mutations never get the disease”. One aim of Lehner’s group is to better understand how the thousands of mutations in the genome of a particular individual interact to influence phenotypes such as disease risk. “What causes the same mutation to have a different outcome in different individuals? That’s one of our favourite questions”, states the researcher. This means understanding how genetics, the environment and ‘chance’ influence the outcome of particular genetic variants.
Recent research has focused on finding ways to predict the ‘normal’ outcome when genes are inhibited. In collaboration with labs from Korea, Toronto and Texas the group has created prediction models using generalisations such as the shared function of genes. “If two proteins interact physically, one can assume that they are involved in similar processes”, Lehner explains. The consequence of this hypothesis is that a mutation in either of the genes is likely to result in a similar phenotype, according to their shared function. By assuming this, one can generalise and expand the findings by using all available information on physical data, genetic interaction and co-evolution of every single gene analysed.
The next step for the group is to understand how the thousands of mutations in an individual’s genome combine to influence their characteristics. “Two humans differ by thousands of mutations, so how do we evaluate the outcome of all of this genetic variation in one go?” Beyond this they are also trying to understand why it can be impossible to predict disease phenotypes from a genome sequence. “Even genetically ‘identical’ twins are not identical when it comes to disease susceptibility. The same is true in simple organisms – if you control the genetics and you control the environment, you still cannot predict what will happen. We are working to understand why this is”, says the head of the lab.
Focus on basic problems
In their studies the group uses experimentally tractable model organisms like yeast or the transparent roundworm C. elegans, but they also use existing data from many different sources. “Rather than working with a single system or approach, we like to choose the best system to study a particular problem with. And if you look at the history of biology, ‘best’ normally means ‘simplest’”, explains the biologist. “The problem is the important thing – it doesn’t really matter how you choose to solve it. But the problem must be basic and general, and one that you can actually solve!”.
This article was published in the El·lipse publication of the PRBB.
Fátima Al-Shahrour, from the CNIO in Madrid, came last week to the PRBB to give a talk entitled “Bioinformatics challenges for personalized medicine”. She explained what they do at her Translational Bioinformatics Unit in the Clinical Research Programme. And what they do is both exciting and promising.
They start with a biopsy of a tumour from a cancer patient who has relapsed after some initial treatment – they concentrate mostly in pancreatic cancer, but it would work with any, in principle. From this sample, they derive cell lines, but also – and they are quite unique in this – they generate a personalised xenograft. That is, they implant the human tumour in an immunocompromised mouse, creating an ‘avatar’ of the patient. After passing it from one mouse to another (they do about 60 mice per patient), they extract the tumour to analyse it by exome sequencing (and sometimes gene expression data, etc). They then have about 8 weeks to find, using bioinformatics, druggable targets that they then test on the avatar. Those drugs that work on the mouse are then given to the patient.
The advantages of this system are many and obvious: not only the in vivo model can be used to validate the hypothesis generated by the genetic analysis, but we basically have a personalised cancer model for a patient in which we can try as many drugs as we want. It can be cryopreserved, so we have unlimited access to the sample. And, since cancer is not a disease we can cure yet, but instead patients must keep checking out for possible relapses, metastasis, or resistances to treatment, keeping the mouse in parallel with the patient can help predicting how the patient will react to all these: whether he will develop resistance to the drug, which other mutations might appear, etc.
But there are several disadvantages, too. One is hinted in Fátima’s talk title: the bioinformatics analysis of the tumours to find which mutations are important (drivers) in the disease and which can have drugs that affect them is challenging, not the least because an individual cancer genome can have hundreds to thousands of mutations.
Perhaps the biggest barrier is that, at the moment, making these avatars is inefficient, very expensive and slow. And since the patients who are benefit from this technology are already in a very bad clinical condition, many of them don’t get to live enough to enjoy those benefits. But there are some successful cases, and Fátima mentioned a couple. In one case, a man with pancreatic cancer who was treated with mitomycin after all the tests in his avatar, survived more than 5 years, when he had been given 1 year at the most.
So there is hope in the field of personalised medicine, despite the fact that this is still not standard, and won’t probably be for the near future. And, as someone in the audience mentioned, in an ideal future, we might even have personalised prevention, according to our genetic makeup. Wouldn’t that be great?
A report by Maruxa Martinez, Scientific Editor at the PRBB
Complex genetic disorders often involve multiple proteins interacting with each other, and pinpointing which of them are actually important for the disease is still challenging. Many computational approaches exploiting interaction network topology have been successfully applied to prioritize which individual genes may be involved in diseases, based on their proximity to known disease genes in the network.
In a paper published in PLoS One, Baldo Oliva, head of the Structural bioinformatics group at the GRIB (UPF–IMIM) and Emre Guney, have presented GUILD (Genes Underlying Inheritance Linked Disorders), a new genome-wide network-based prioritization framework. GUILD includes four novel algorithms that use protein-protein interaction data to predict gene-phenotype associations at genome-wide scale, and the authors have proved that they are comparable, or outperform, several known state-of-the-art similar approaches.
As a proof of principle, the authors have used GUILD to investigate top-ranking genes in Alzheimer’s disease (AD), diabetes and AIDS using disease-gene associations from various sources.
GUILD is freely available for download at http://sbi.imim.es/GUILD.php
Guney E, Oliva B. Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization. PLoS One. 2012;7(9):e43557
The symposium will focus on the latest and most important advances in genomics but also in genetics, molecular and cell biology, or biotechnology. Several scientists in the international arena, such as Angus LAMOND (Wellcome Trust Centre for Gene Regulation and Expression, Dundee, UK), Tom MANIATIS (Columbia University, New York, US), or Iain MATTAJ (EMBL Heidelberg, Germany) will showcase the achievements of the CRG in the last 10 years in these fields. You can check here the full program.
Registration is free of charge, but finishes next Oct 8, so hurry up!
The May 2012 edition of the PRBB newspaper, El.lipse, a monthly bilingual newspaper, is now available: http://bit.ly/KprWYh
Is working at night harmful? This is one of the issues that the group of Manolis Kogevines (CREAL) is addressing in its research as explained in the new issue of El·lipse. You could also learn about the cutting-edge work with cord blood stem cells for transplants by Nadim Mahumd from the University of Illinois. The genetic origin of Afghanistan’s ethnic groups, the results of the most ambitious genetic study so far about osteoporosis and a European map of mental disorders are among the news that can be discovered in this month’s newspaper. But you could also learn about the book exchange initiative at the PRBB and also how to make a delicious aubergines “a la parmigiana” meal. Don’t miss it!
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]