Periodic Reporting for period 3 - SyBil-AA (Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy)
Berichtszeitraum: 2019-01-01 bis 2019-12-31
In the EU, an adult consumes on average 11 liters of pure alcohol each year – more than in any other region in the world. The impact on society is enormous. Not only is alcohol linked to many non-communicable diseases, including cancers, cardiovascular diseases, liver cirrhosis, neuropsychiatric disorders and fetal alcohol syndrome. It also leads to alcohol addiction – also called alcoholism – the most severe form of alcohol use disorders. Nearly 20 million European citizens may suffer from this disorder which leads to a much larger loss in average life expectancy than life-time smoking.
Alcohol addiction is characterized by cycles of excessive alcohol consumption, interspersed with intervals of abstinence, and frequent relapses. The vulnerability to relapse is a key element of the disease process and blocking relapse is therefore the most important treatment objective. Despite many efforts, the outcomes for even the best studied treatment programs are not impressive, with about half of the patients relapsing within a year. The few pharmacotherapies available for alcohol addiction have not changed this situation because these medications have small effect sizes and only a minority of the patients do benefit from such treatments.
The urgency to understand more about the changes in the brain of alcohol addicts and to find effective treatments is obvious. Considerable research efforts have been made all over the world and led to increased understanding of the psychological and neurobiological processes that underlie addictive disorders. Despite substantial enthusiasm about the prospect of developing novel, mechanism-based therapies, these prospects have not played out so far, indicating the challenge of translating scientific knowledge into the clinical praxis of addiction therapy. While this translational gap has complex causes, we think that one important limitation of the current neurobiological view on alcohol addiction is the focus on only a few processes and brain regions, albeit the broad systemic actions of alcohol are commonly acknowledged.
The SyBil-AA project addressed this problem by adopting a global perspective on brain network organization – the connectome – by making use of mathematical and network theoretical methods. These advanced computational tools allowed us to better describe the dynamics of brain networks in the addicted state and to compare these with healthy brains. Predictive models of ‘relapse-prone’ states of the brain connectome were built based on magnetic resonance imaging (MRI) studies in patient populations and healthy control subjects. MRI is widely employed in basic and clinical research to map both the functional and structural organization of the brain. Hence, MRI is principally well suited to obtain a global view on brain activity. MRI information from humans and laboratory animals is complemented by electrophysiology and neurochemical data. For this, we systematically mined public knowledge databases that contain experimental reports from decades of research. Our highly interdisciplinary team included leading experts on animal models of alcoholism with the ability to ascertain molecular and cellular processes in great detail. The concepts develop by this group were then validated and extended in human brain imaging and intervention studies by the most established clinical alcohol researchers in Europe. Furthermore, several young teams with outstanding expertise in systems biology and network science joined to analyze the findings from the animal and human experiments. They proposed connectome models for the alcoholic brain as well as potential access points for guiding the network to more normal functioning. Together, we achieved our translational goal by setting up a theoretical and experimental framework for making predictions based on MRI and mathematical modeling, which is verified in animals, and which was transferred to human research. Thereby we provide a rational discovery strategy based on the principles of systems medicine for design and development of novel, evidence based therapies for alcohol addiction.
Alcohol addiction is characterized by cycles of excessive alcohol consumption, interspersed with intervals of abstinence, and frequent relapses. The vulnerability to relapse is a key element of the disease process and blocking relapse is therefore the most important treatment objective. Despite many efforts, the outcomes for even the best studied treatment programs are not impressive, with about half of the patients relapsing within a year. The few pharmacotherapies available for alcohol addiction have not changed this situation because these medications have small effect sizes and only a minority of the patients do benefit from such treatments.
The urgency to understand more about the changes in the brain of alcohol addicts and to find effective treatments is obvious. Considerable research efforts have been made all over the world and led to increased understanding of the psychological and neurobiological processes that underlie addictive disorders. Despite substantial enthusiasm about the prospect of developing novel, mechanism-based therapies, these prospects have not played out so far, indicating the challenge of translating scientific knowledge into the clinical praxis of addiction therapy. While this translational gap has complex causes, we think that one important limitation of the current neurobiological view on alcohol addiction is the focus on only a few processes and brain regions, albeit the broad systemic actions of alcohol are commonly acknowledged.
The SyBil-AA project addressed this problem by adopting a global perspective on brain network organization – the connectome – by making use of mathematical and network theoretical methods. These advanced computational tools allowed us to better describe the dynamics of brain networks in the addicted state and to compare these with healthy brains. Predictive models of ‘relapse-prone’ states of the brain connectome were built based on magnetic resonance imaging (MRI) studies in patient populations and healthy control subjects. MRI is widely employed in basic and clinical research to map both the functional and structural organization of the brain. Hence, MRI is principally well suited to obtain a global view on brain activity. MRI information from humans and laboratory animals is complemented by electrophysiology and neurochemical data. For this, we systematically mined public knowledge databases that contain experimental reports from decades of research. Our highly interdisciplinary team included leading experts on animal models of alcoholism with the ability to ascertain molecular and cellular processes in great detail. The concepts develop by this group were then validated and extended in human brain imaging and intervention studies by the most established clinical alcohol researchers in Europe. Furthermore, several young teams with outstanding expertise in systems biology and network science joined to analyze the findings from the animal and human experiments. They proposed connectome models for the alcoholic brain as well as potential access points for guiding the network to more normal functioning. Together, we achieved our translational goal by setting up a theoretical and experimental framework for making predictions based on MRI and mathematical modeling, which is verified in animals, and which was transferred to human research. Thereby we provide a rational discovery strategy based on the principles of systems medicine for design and development of novel, evidence based therapies for alcohol addiction.
Several high-profile publications have emanated from the SyBil-AA project. For example, in a proof-of-concept study published in PNAS (Hirth et al. 2016) we demonstrated the strong benefit that can be gained from systems theoretical approaches for experimental work. We could provide a new model for describing the dynamics of the neurotransmitter dopamine during abstinence from alcohol drinking. Elucidating the causes and consequences associated with the changing dopamine levels could identify ways to understand, and thus curtail, relapse. In two seminal papers published in PLoS Biology (Horvát et al. 2016; Noori et al. 2017), systems researchers from the SyBil-AA consortium unraveled universal principles of network organization across brains of different mammalian orders (e.g. monkey, rat, mouse). This finding is highly important for the translation of network properties from rodents to humans and reverse. The other crucial aspect of this work was the generation of a new, publicly available connectome database of the rat (ChemNetDB.org) that organizes half a century’s neurochemical and neuroanatomical measurements from over 35.000 rats. It has significant advance over existing databases, thus ChemNetDB is currently the most comprehensive multi-scale database integrating neurochemical information in a consistent and validated manner. Lastly, in a recent MRI study in treatment seeking patients published in JAMA Psychiatry (de Santis et al 2019), we discovered that alcohol’s known detrimental and widespread effects on brain white matter are progressing even in the early weeks after quitting and that persistent brain deficits due to excessive alcohol consumption can occur much earlier than is currently believed. The early signs of brain problems due to alcohol cannot be detected in standard MRI scans, but we are now working to develop a screening method based on advanced magnetic resonance imaging. Together, SyBil-AA researchers have published more than 30 articles, many of these reporting shared work from several partners illustrating the functioning and productivity of the consortium.
The SyBil-AA team could integrate fragmented research efforts on alcoholism across leading laboratories and clinics in Europe, and to combine this with leading expertise in mathematical modelling. Rapid knowledge transfer between these specialized research groups enabled us to accomplish a goal not feasible for any one of the participants alone: to identify brain connectome alterations that can point to novel alcoholism treatments and to biomarkers that are predictive of clinical efficacy. These are important steps towards a personalized medicine approach to better help the individual patient.
In a wider perspective, our interdisciplinary consortium will also have strong impact on the way we will carried out research in the future. A new generation of scientists has been taught to better understand the overall picture, to communicate more easily with scientists from different fields, and to apply their own expertise within such a framework.
In a wider perspective, our interdisciplinary consortium will also have strong impact on the way we will carried out research in the future. A new generation of scientists has been taught to better understand the overall picture, to communicate more easily with scientists from different fields, and to apply their own expertise within such a framework.