A unified resource and configurable model of the synapse proteome and its role in disease

Genes encoding synaptic proteins are highly associated with neuronal disorders many of which show clinical co-morbidity. We integrated 58 published synaptic proteomic datasets that describe over 8000 proteins and combined them with direct protein–protein interactions and functional metadata to build a network resource that reveals the shared and unique protein components that underpin multiple disorders. All the data are provided in a flexible and accessible format to encourage custom use

Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR\u27s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives

Community-driven ELIXIR activities in single-cell omics

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research. Keywor

LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases

The current epidemics of cardiovascular and metabolic noncommunicable diseases have emerged alongside dramatic modifications in lifestyle and living environments. These correspond to changes in our “modern” postwar societies globally characterized by rural-to-urban migration, modernization of agricultural practices, and transportation, climate change, and aging. Evidence suggests that these changes are related to each other, although the social and biological mechanisms as well as their interactions have yet to be uncovered. LongITools, as one of the 9 projects included in the European Human Exposome Network, will tackle this environmental health equation linking multidimensional environmental exposures to the occurrence of cardiovascular and metabolic noncommunicable diseases.</p

A Systems Biological Approach to Parkinson's Disease

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. Itshows a high degree of genetic and phenotypic complexity with many implicated factors, various diseasemanifestations but few clear causal links. Ongoing research has identified a growing number of molecularalterations linked to the disease.Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD.Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potentialgenetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area isdata quality and accessibility.In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associatedgenes could be identified, based on mutations and curated annotations. I curated an up-to-date and completesynaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing thepresynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigatingsimilarities and differences, including reproducibility and functional interpretations.The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledgeregarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specificProtein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases.Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set oflow level statistics, including modularity, clustering coefficient and node degree, explaining the network’stopology from a mathematical point of view were obtained.Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functionalnetwork subgroups, different clustering algorithms were investigated. In the context of biological networks, theunderlying hypothesis is that proteins in a structural community are more likely to share common functions.Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they werecompared amongst each other. Three community clusters could be identified as containing largely overlappinggene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in thesecommunities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specificbiological processes and cellular components, which include neurotransmitter secretion, positive regulation ofsynapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junctiondevelopment and complement activation (classical pathway) amongst others.The presented approach combined a curated set of PD associated genes, filtered PPI information andsynaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis,clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions.Specific disease associated functions confirmed known research insights and allowed me to propose a newlist of so far unknown potential disease associated genes. Due to the open design, this approach can be usedto answer similar research questions regarding other complex diseases amongst others.Parkinsons sjukdom (PS) är den näst vanligaste neurodegenerativa sjukdomen i väst-världen. Den uppvisar en hög grad av genetisk och fenotypisk komplexitet, med mångafaktorer inblandade, den manifesterar sig på olika sätt men det finns få identifieradeorsakssamband. Forskningen har dock identifierat fler och fler molekylära förändringarsom är kopplade till sjukdomen.Det är främst dopaminerga nervceller i Substantia Nigra och speciellt deras synap-ser, som är påverkade vid PS. Denna avhandling fokuserar därför på att förstå desynaptiska sjukdomseffekterna bättre, och arbetet har som ambition att identifiera genetisktorsakade och synaptiskt associerade PS-mekanismer. Idag är en huvudsaklig utmaninginom detta fält kvaliteten på och åtkomsten av data.I denna studie inhämtades och analyserades publikt data på ett systematiskt sätt, föratt bättre förstå PS. 418 PS-associerade gener kunde identifieras baserat på mutationersamt kurerade annoteringar av data. Jag kurerade ett synapsproteom som innehöll to-talt 6706 proteiner. Regionspecifika datamängder som beskriver presynapsen, postsy-napsen och hela synapsregionen definierades också. Dessa datamängder analyseradesgenom att undersöka likheter och skillnader, inklusive reproducerbarhet av resultatensamt proteinernas funktionella kopplingar.Analys av Protein-Protein-Interaktions Nätverk (PPIN) valdes för att få en dju-pare kunskap vad gäller specifika effekter av PS på synapsen. Därför genereradejag ett specifikt Protein-Protein-Interaktions (PPI dataset från fyra publika databasersom innehöll 211824 direkta interaktioner. Proteomikdata och PPI-information fråndetta dataset skapade förutsättningar för att konstruera några PPINs. Dessa anal-yserades och statistisk information erhölls vad gäller nätverkens modularitet, klus-tringskoefficient och nodgrad, vilket beskriver nätverkens topologi utifrån en matem-atisk synvinkel. Förutom denna grundläggande nätverksstatistik, undersöktes ocksåmer specifika egenskaper vad gäller topologin i de studerade PPINs. För att under-söka funktionella undergrupper i nätverken användes olika klustringsalgoritmer. Närdet gäller biologiska nätverk är den underliggande hypotesen att proteiner som ingår isamma strukturella grupp har högre sannolikhet att ha gemensamma funktioner. Där-för försökte jag identifiera grupper anrikade med synapsproteiner som associeras medPS. Dessa grupper jämfördes sedan med varandra. Tre kluster identifierades vilka in-nehöll överlappande uppsättningar gener. Dessa innehöll 24 gener associerade med PS.Förutom de gener som man redan vet är associerade med sjukdomen, identifierade viytterligare 322 potentiella gener. För vart och ett av dessa tre kluster var förekomsten  av vissa biologiska processer och cellulära komponenter förhöjd, såsom sådana somär involverade i den synaptiska transmissionen eller reglerar pre- och postsynapsensproteinorganisation, inklusive t.e.x. “scaffold” proteiner och klassisk komplementak-tivering (relaterat till immunystemet).Det beskrivna tillvägagångssättet kombinerar en kurerad datamängd av gener as-socierade med PS, filtrerad PPI information och synapsproteom. Olika små- ochstorskaliga analytiska metoder, såsom topologianalys av PPINs, klustringsalgoritmeroch anrikningsstudier identifierade synapsproteiner som var påverkade vid PS. Despecifika sjukdomsassocierade proteinfunktioner som identifierades bekräftade redanexisterande kunskap, och tillät mig att lista tidigare okända potentiella sjukdomsas-socierade gener. Ett liknande tillvägagångssätt kan i framtiden även appliceras på an-dra komplexa sjukdomar.QC 20180403</p

A Systems Biological Approach to Parkinson's Disease

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. Itshows a high degree of genetic and phenotypic complexity with many implicated factors, various diseasemanifestations but few clear causal links. Ongoing research has identified a growing number of molecularalterations linked to the disease.Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD.Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potentialgenetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area isdata quality and accessibility.In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associatedgenes could be identified, based on mutations and curated annotations. I curated an up-to-date and completesynaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing thepresynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigatingsimilarities and differences, including reproducibility and functional interpretations.The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledgeregarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specificProtein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases.Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set oflow level statistics, including modularity, clustering coefficient and node degree, explaining the network’stopology from a mathematical point of view were obtained.Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functionalnetwork subgroups, different clustering algorithms were investigated. In the context of biological networks, theunderlying hypothesis is that proteins in a structural community are more likely to share common functions.Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they werecompared amongst each other. Three community clusters could be identified as containing largely overlappinggene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in thesecommunities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specificbiological processes and cellular components, which include neurotransmitter secretion, positive regulation ofsynapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junctiondevelopment and complement activation (classical pathway) amongst others.The presented approach combined a curated set of PD associated genes, filtered PPI information andsynaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis,clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions.Specific disease associated functions confirmed known research insights and allowed me to propose a newlist of so far unknown potential disease associated genes. Due to the open design, this approach can be usedto answer similar research questions regarding other complex diseases amongst others.Parkinsons sjukdom (PS) är den näst vanligaste neurodegenerativa sjukdomen i väst-världen. Den uppvisar en hög grad av genetisk och fenotypisk komplexitet, med mångafaktorer inblandade, den manifesterar sig på olika sätt men det finns få identifieradeorsakssamband. Forskningen har dock identifierat fler och fler molekylära förändringarsom är kopplade till sjukdomen.Det är främst dopaminerga nervceller i Substantia Nigra och speciellt deras synap-ser, som är påverkade vid PS. Denna avhandling fokuserar därför på att förstå desynaptiska sjukdomseffekterna bättre, och arbetet har som ambition att identifiera genetisktorsakade och synaptiskt associerade PS-mekanismer. Idag är en huvudsaklig utmaninginom detta fält kvaliteten på och åtkomsten av data.I denna studie inhämtades och analyserades publikt data på ett systematiskt sätt, föratt bättre förstå PS. 418 PS-associerade gener kunde identifieras baserat på mutationersamt kurerade annoteringar av data. Jag kurerade ett synapsproteom som innehöll to-talt 6706 proteiner. Regionspecifika datamängder som beskriver presynapsen, postsy-napsen och hela synapsregionen definierades också. Dessa datamängder analyseradesgenom att undersöka likheter och skillnader, inklusive reproducerbarhet av resultatensamt proteinernas funktionella kopplingar.Analys av Protein-Protein-Interaktions Nätverk (PPIN) valdes för att få en dju-pare kunskap vad gäller specifika effekter av PS på synapsen. Därför genereradejag ett specifikt Protein-Protein-Interaktions (PPI dataset från fyra publika databasersom innehöll 211824 direkta interaktioner. Proteomikdata och PPI-information fråndetta dataset skapade förutsättningar för att konstruera några PPINs. Dessa anal-yserades och statistisk information erhölls vad gäller nätverkens modularitet, klus-tringskoefficient och nodgrad, vilket beskriver nätverkens topologi utifrån en matem-atisk synvinkel. Förutom denna grundläggande nätverksstatistik, undersöktes ocksåmer specifika egenskaper vad gäller topologin i de studerade PPINs. För att under-söka funktionella undergrupper i nätverken användes olika klustringsalgoritmer. Närdet gäller biologiska nätverk är den underliggande hypotesen att proteiner som ingår isamma strukturella grupp har högre sannolikhet att ha gemensamma funktioner. Där-för försökte jag identifiera grupper anrikade med synapsproteiner som associeras medPS. Dessa grupper jämfördes sedan med varandra. Tre kluster identifierades vilka in-nehöll överlappande uppsättningar gener. Dessa innehöll 24 gener associerade med PS.Förutom de gener som man redan vet är associerade med sjukdomen, identifierade viytterligare 322 potentiella gener. För vart och ett av dessa tre kluster var förekomsten  av vissa biologiska processer och cellulära komponenter förhöjd, såsom sådana somär involverade i den synaptiska transmissionen eller reglerar pre- och postsynapsensproteinorganisation, inklusive t.e.x. “scaffold” proteiner och klassisk komplementak-tivering (relaterat till immunystemet).Det beskrivna tillvägagångssättet kombinerar en kurerad datamängd av gener as-socierade med PS, filtrerad PPI information och synapsproteom. Olika små- ochstorskaliga analytiska metoder, såsom topologianalys av PPINs, klustringsalgoritmeroch anrikningsstudier identifierade synapsproteiner som var påverkade vid PS. Despecifika sjukdomsassocierade proteinfunktioner som identifierades bekräftade redanexisterande kunskap, och tillät mig att lista tidigare okända potentiella sjukdomsas-socierade gener. Ett liknande tillvägagångssätt kan i framtiden även appliceras på an-dra komplexa sjukdomar.QC 20180403</p

Automated AFM analysis of DNA bending reveals initial lesion sensing strategies of DNA glycosylases

Base excision repair is the dominant DNA repair pathway of chemical modifications such as deamination, oxidation, or alkylation of DNA bases, which endanger genome integrity due to their high mutagenic potential. Detection and excision of these base lesions is achieved by DNA glycosylases. To investigate the remarkably high efficiency in target site search and recognition by these enzymes, we applied single molecule atomic force microscopy (AFM) imaging to a range of glycosylases with structurally different target lesions. Using a novel, automated, unbiased, high-throughput analysis approach, we were able to resolve subtly different conformational states of these glycosylases during DNA lesion search. Our results lend support to a model of enhanced lesion search efficiency through initial lesion detection based on altered mechanical properties at lesions. Furthermore, its enhanced sensitivity and easy applicability also to other systems recommend our novel analysis tool for investigations of diverse, fundamental biological interactions