Kodierung enzymatischer Reaktionen

Die Einteilung enzymatischer Reaktionen erfolgt auf Basis des EC-Klassifikationssystems. Die Einordnung neuer Enzyme ist hochkomplex und erfordert die Absprache von verschiedenen Enzymkommissionen der Organisationen IUPAC und IUBMB. Dieses zeitaufwendige Verfahren war die Motivation für die vorliegende Arbeit, ein automatisiertes System für die Charakterisierung enzymatischer Reaktionen zu entwickeln, das auf dem Dugundji-Ugi-Modell basiert. Hierbei werden Reaktionen durch mathematische Operatoren beschrieben, die als Reaktionsmatrizen (R-Matrizen) bezeichnet werden und das Elektronentransfermuster einer Reaktion kodieren. R-Matrizen enthalten die Information, welche Bindungen gespalten werden oder entstehen und welche Atome an der Reaktion beteiligt sind. Das Errechnen der R-Matrizen erfordert allerdings eine Atomzuordnung der Eduktatome auf die Produktatome. Diese Zuordnung wurde in der Vergangenheit immer mit hohem manuellem Aufwand erstellt. Im Rahmen dieser Arbeit wurde ein Programm entwickelt, das die R-Matrizen nur anhand der Edukt- und Produktmoleküle errechnet und ohne manuelle Unterstützung auskommt. Kern des Verfahrens ist ein MCS-Algorithmus (Maximal Common Subgraph), der die maximalen gemeinsamen Substrukturen von 2 Molekülen errechnet. Dieser neu entwickelte Algorithmus ist sehr flexibel und kann daher auch komplexe Veränderungen in der Molekülstruktur nachvollziehen. Er wird dazu verwendet, die Eduktmoleküle mit den Produktmolekülen zu vergleichen. Die maximalen gemeinsamen Substrukturen aus den Molekülvergleichen werden kombiniert und durch ein Rankingsystem bewertet, das alle möglichen Atomzuordnungen errechnet. Bedingt durch die Komplexität vieler biochemischer Reaktionen, war es erforderlich, das Verfahren um weitere Algorithmen zu erweitern, um falsche Zuordnungen zu vermeiden und die Laufzeit zu verbessern. So wurden Verfahren zur Erkennung von lokalen Symmetrien, aromatischen Ringsystemen und Kofaktoren integriert und eine weitere Molekülvergleichsebene eingeführt. Mit Hilfe dieses Systems von verschiedenen Algorithmen wird eine vollständige Atomzuordnung generiert. Auf Grundlage einer vollständigen Atomzuordnung ist die Berechnung der R-Matrizen schließlich sehr einfach. Ein weiteres Problem stellte allerdings der Vergleich der R-Matrizen dar, weil von jeder R-Matrix n! mögliche Permutationen erzeugt werden können. Der Vergleich erfolgt mit Hilfe eines neuen Kanonisierungsalgorithmus, der aus der Vielzahl von Permutationen eine repräsentative R-Matrix selektiert. Die Überführung der R-Matrizen in R-Strings vereinfacht schließlich den Vergleich und die Gruppierung von enzymatischen Reaktionen anhand ihres Elektronentransfermusters. Auf der Grundlage eines Datensets, das 228 der 229 definierten Subsubklassen abdeckt, wurden die R-Matrizen von 3209 enzymatischen Reaktionen automatisch errechnet. Neben der Analyse der R-Matrizen selbst, standen bei der Auswertung die Beziehungen zwischen dem Dugundji-Ugi-Modell und dem EC-Klassifikationssystem im Vordergrund. Als Bezugspunkt wurden die Subsubklassen des EC-Klassifikationssystems gewählt, die bereits soweit spezifiziert sind, dass hier eine hohe Übereinstimmung beider Systeme zu erwarten war. So wurden die Subsubklassen auf ihre Homogenität in ihren Elektronentransfermustern untersucht. Die häufigsten R-Matrizen jeder Subsubklasse wurden verglichen und Subsubklassen mit identischen R-Matrizen gruppiert. Die Elektronentransfermuster innerhalb der Subsubklassen erwiesen sich in der Mehrzahl als homogen. Allerdings wurden auch Subsubklassen mit geringer Übereinstimmung in den Elektronentransfermustern gefunden, für die eine höhere Homogenität erwartet worden wäre. Der R-Matrixvergleich der verschiedenen Subsubklassen ergab, dass 121 Subsubklassen bereits ein spezifisches Elektronentransfermuster besitzen. 107 Subsubklassen bilden hingegen Gruppen verschiedener Größe. Einige Gruppen bestehen aus Subsubklassen von verschiedenen Hauptklassen, obwohl sie nach ihren Elektronentransfermustern einen identischen Reaktionskern besitzen. Die Ergebnisse machen die Eigenschaften des Dugundji-Ugi-Modells deutlich, das sehr rational und unvoreingenommen die wesentlichsten Eigenschaften einer Reaktion beschreibt. Ähnlich, wie das EC-Klassifikationssystem, betrachtet es aber die chemischen Eigenschaften der Gesamtreaktion. Es könnte daher die Enzymklassifikation unterstützen, indem es aufzeigt, welche Subsubklassen feiner untergliedert oder zu größeren Gruppen zusammengefasst werden könnten

Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography

Purpose The amount of atherosclerotic plaque and its components (calcifications, fibrous tissue, and lipid core) could be better predictors of acute events than the now currently used degree of stenosis. Therefore, we evaluated a dedicated software tool for volume measurements of atherosclerotic carotid plaque and its components in multidetector computed tomography angiography (MDCTA) images. Materials and Methods Data acquisition was approved by the Institutional Review Board and all patients gave written informed consent. MDCTA images of 56 carotid arteries were analyzed by three observers. Plaque volumes were assessed by manual drawing of the outer vessel contour. The luminal boundary was determined based on a Hounsfield-Unit (HU) threshold. The contribution of different components was measured by the number of voxels within defined ranges of HU-values (calcification >130 HU, fibrous tissue 60–130 HU, lipid core <60 HU). Interobserver variability (IOV) was assessed. Results Plaque volume was 1,259 ± 621 mm3. The calcified, fibrous and lipid volumes were 238 ± 252 mm3, 647 ± 277 mm3 and 376 ± 283 mm3, respectively. IOV was moderate with interclass correlation coefficients (ICC) ranging from 0.76 to 0.99 and coefficients of variation (COV) ranging from 3% to 47%. Conclusion Atherosclerotic carotid plaque volume and plaque component volumes can be assessed with MDCTA with a reasonable observer variability

Follow-up of loci from the International Genomics of Alzheimer's Disease Project identifies TRIP4 as a novel susceptibility gene

To follow-up loci discovered by the International Genomics of Alzheimer's Disease Project, we attempted independent replication of 19 single nucleotide polymorphisms (SNPs) in a large Spanish sample (Fundació ACE data set; 1808 patients and 2564 controls). Our results corroborate association with four SNPs located in the genes INPP5D, MEF2C, ZCWPW1 and FERMT2, respectively. Of these, ZCWPW1 was the only SNP to withstand correction for multiple testing (P=0.000655). Furthermore, we identify TRIP4 (rs74615166) as a novel genome-wide significant locus for Alzheimer's disease risk (odds ratio=1.31; confidence interval 95% (1.19-1.44); P=9.74 × 10 - 9)

Fachkräftebedarf: Analyse und Handlungsstrategien

Mit der verbesserten Lage am Arbeitsmarkt und dem aus demografischen Gründen zu erwartenden Rückgang des Erwerbspersonenpotenzials gewinnt auch das Thema Fachkräftesicherung immer mehr an Bedeutung. Dieses Thema wird in Kapitel D ("Fachkräftebedarf: Analyse und Handlungsstrategien") eingehend behandelt. Dabei wird deutlich: Die Folgen des demografischen Wandels für den Arbeitsmarkt sind erheblich und es muss an vielen Stellschrauben gedreht werden, um diese abzumildern. Sowohl die Mobilisierung inländischer Potenziale als auch die verstärkte Zuwanderung von Fachkräften ist notwendig, um den Rückgang des Erwerbspotenzials spürbar abzufedern

Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Peer reviewe

Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32

Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% and account for 20-30% of all epilepsies. Despite their high heritability of 80%, the genetic factors predisposing to GGEs remain elusive. To identify susceptibility variants shared across common GGE syndromes, we carried out a two-stage genome-wide association study (GWAS) including 3020 patients with GGEs and 3954 controls of European ancestry. To dissect out syndrome-related variants, we also explored two distinct GGE subgroups comprising 1434 patients with genetic absence epilepsies (GAEs) and 1134 patients with juvenile myoclonic epilepsy (JME). Joint Stage-1 and 2 analyses revealed genome-wide significant associations for GGEs at 2p16.1 (rs13026414, Pmeta = 2.5 × 10−9, OR[T] = 0.81) and 17q21.32 (rs72823592, Pmeta = 9.3 × 10−9, OR[A] = 0.77). The search for syndrome-related susceptibility alleles identified significant associations for GAEs at 2q22.3 (rs10496964, Pmeta = 9.1 × 10−9, OR[T] = 0.68) and at 1q43 for JME (rs12059546, Pmeta = 4.1 × 10−8, OR[G] = 1.42). Suggestive evidence for an association with GGEs was found in the region 2q24.3 (rs11890028, Pmeta = 4.0 × 10−6) nearby the SCN1A gene, which is currently the gene with the largest number of known epilepsy-related mutations. The associated regions harbor high-ranking candidate genes: CHRM3 at 1q43, VRK2 at 2p16.1, ZEB2 at 2q22.3, SCN1A at 2q24.3 and PNPO at 17q21.32. Further replication efforts are necessary to elucidate whether these positional candidate genes contribute to the heritability of the common GGE syndrome

A systematic review of progranulin concentrations in biofluids in over 7,000 people—assessing the pathogenicity of GRN mutations and other influencing factors

Background: Pathogenic heterozygous mutations in the progranulin gene (GRN) are a key cause of frontotemporal dementia (FTD), leading to significantly reduced biofluid concentrations of the progranulin protein (PGRN). This has led to a number of ongoing therapeutic trials aiming to treat this form of FTD by increasing PGRN levels in mutation carriers. However, we currently lack a complete understanding of factors that affect PGRN levels and potential variation in measurement methods. Here, we aimed to address this gap in knowledge by systematically reviewing published literature on biofluid PGRN concentrations. Methods: Published data including biofluid PGRN concentration, age, sex, diagnosis and GRN mutation were collected for 7071 individuals from 75 publications. The majority of analyses (72%) had focused on plasma PGRN concentrations, with many of these (56%) measured with a single assay type (Adipogen) and so the influence of mutation type, age at onset, sex, and diagnosis were investigated in this subset of the data. Results: We established a plasma PGRN concentration cut-off between pathogenic mutation carriers and non-carriers of 74.8 ng/mL using the Adipogen assay based on 3301 individuals, with a CSF concentration cut-off of 3.43 ng/mL. Plasma PGRN concentration varied by GRN mutation type as well as by clinical diagnosis in those without a GRN mutation. Plasma PGRN concentration was significantly higher in women than men in GRN mutation carriers (p = 0.007) with a trend in non-carriers (p = 0.062), and there was a significant but weak positive correlation with age in both GRN mutation carriers and non-carriers. No significant association was seen with weight or with TMEM106B rs1990622 genotype. However, higher plasma PGRN levels were seen in those with the GRN rs5848 CC genotype in both GRN mutation carriers and non-carriers. Conclusions: These results further support the usefulness of PGRN concentration for the identification of the large majority of pathogenic mutations in the GRN gene. Furthermore, these results highlight the importance of considering additional factors, such as mutation type, sex and age when interpreting PGRN concentrations. This will be particularly important as we enter the era of trials for progranulin-associated FTD.</p

Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder

Background Bipolar disorder (BD) is a common and highly heritable disorder of mood. Genome-wide association studies (GWAS) have identified several independent susceptibility loci. In order to extract more biological information from GWAS data, multi-locus approaches represent powerful tools since they utilize knowledge about biological processes to integrate functional sets of genes at strongly to moderately associated loci. Methods We conducted gene set enrichment analyses (GSEA) using 2.3 million single-nucleotide polymorphisms, 397 Reactome pathways and 24,025 patients with BD and controls. RNA expression of implicated individual genes and gene sets were examined in post-mortem brains across lifespan. Results Two pathways showed a significant enrichment after correction for multiple comparisons in the GSEA: GRB2 events in ERBB2 signaling, for which 6 of 21 genes were BD associated (P = 0.0377), and NCAM signaling for neurite out-growth, for which 11 out of 62 genes were BD associated (P = 0.0451). Most pathway genes showed peaks of RNA co-expression during fetal development and infancy and mapped to neocortical areas and parts of the limbic system. Limitations Pathway associations were technically reproduced by two methods, although they were not formally replicated in independent samples. Gene expression was explored in controls but not in patients. Conclusions Pathway analysis in large GWAS data of BD and follow-up of gene expression patterns in healthy brains provide support for an involvement of neurodevelopmental processes in the etiology of this neuropsychiatric disease. Future studies are required to further evaluate the relevance of the implicated genes on pathway functioning and clinical aspects of BD

Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder

Bipolar disorder (BD) is a common and highly heritable disorder of mood. Genome-wide association studies (GWAS) have identified several independent susceptibility loci. In order to extract more biological information from GWAS data, multi-locus approaches represent powerful tools since they utilize knowledge about biological processes to integrate functional sets of genes at strongly to moderately associated loci.We conducted gene set enrichment analyses (GSEA) using 2.3 million single-nucleotide polymorphisms, 397 Reactome pathways and 24,025 patients with BD and controls. RNA expression of implicated individual genes and gene sets were examined in post-mortem brains across lifespan.Two pathways showed a significant enrichment after correction for multiple comparisons in the GSEA: GRB2 events in ERBB2 signaling, for which 6 of 21 genes were BD associated (PFDR = 0.0377), and NCAM signaling for neurite out-growth, for which 11 out of 62 genes were BD associated (PFDR = 0.0451). Most pathway genes showed peaks of RNA co-expression during fetal development and infancy and mapped to neocortical areas and parts of the limbic system.Pathway associations were technically reproduced by two methods, although they were not formally replicated in independent samples. Gene expression was explored in controls but not in patients.Pathway analysis in large GWAS data of BD and follow-up of gene expression patterns in healthy brains provide support for an involvement of neurodevelopmental processes in the etiology of this neuropsychiatric disease. Future studies are required to further evaluate the relevance of the implicated genes on pathway functioning and clinical aspects of BD