Small intestinal bacterial overgrowth mimicking acute flare as a pitfall in patients with Crohn's Disease

<p>Abstract</p> <p>Background</p> <p>Small intestinal bacterial overgrowth (SIBO) is characterized by excessive proliferation of colonic bacterial species in the small bowel. Potential causes of SIBO include fistulae, strictures or motility disturbances. Hence, patients with Crohn's Disease (CD) are especially predisposed to develop SIBO. As result, CD patients may experience malabsorption and report symptoms such as weight loss, watery diarrhea, meteorism, flatulence and abdominal pain, mimicking acute flare in these patients.</p> <p>Methods</p> <p>One-hundred-fifty patients with CD reporting increased stool frequency, meteorism and/or abdominal pain were prospectively evaluated for SIBO with the Hydrogen Glucose Breath Test (HGBT).</p> <p>Results</p> <p>Thirty-eight patients (25.3%) were diagnosed with SIBO based on positive findings at HGBT. SIBO patients reported a higher rate of abdominal complaints and exhibited increased stool frequency (5.9 vs. 3.7 bowel movements/day, p = 0.003) and lower body weight (63.6 vs 70.4 kg, p = 0.014). There was no correlation with the Crohn's Disease Activity Index. SIBO was significantly more frequent in patients with partial resection of the colon or multiple intestinal surgeries; there was also a clear trend in patients with ileocecal resection that did not reach statistical significance. SIBO rate was also higher in patients with affection of both the colon and small bowel, while inflammation of the (neo)terminal ileum again showed only tendential association with the development of SIBO.</p> <p>Conclusion</p> <p>SIBO represents a frequently ignored yet clinically relevant complication in CD, often mimicking acute flare. Because symptoms of SIBO are often difficult to differentiate from those caused by the underlying disease, targeted work-up is recommended in patients with corresponding clinical signs and predisposing factors.</p

A rare loss-of-function variant of ADAM17 is associated with late-onset familial Alzheimer disease

Common variants of about 20 genes contributing to AD risk have so far been identified through genome-wide association studies (GWAS). However, there is still a large proportion of heritability that might be explained by rare but functionally important variants. One of the so far identified genes with rare AD causing variants is ADAM10. Using whole-genome sequencing we now identified a single rare nonsynonymous variant (SNV) rs142946965 [p.R215I] in ADAM17 co-segregating with an autosomal-dominant pattern of late-onset AD in one family. Subsequent genotyping and analysis of available whole-exome sequencing data of additional case/control samples from Germany, UK, and USA identified five variant carriers among AD patients only. The mutation inhibits pro-protein cleavage and the formation of the active enzyme, thus leading to loss-of-function of ADAM17 alpha-secretase. Further, we identified a strong negative correlation between ADAM17 and APP gene expression in human brain and present in vitro evidence that ADAM17 negatively controls the expression of APP. As a consequence, p.R215I mutation of ADAM17 leads to elevated Aß formation in vitro. Together our data supports a causative association of the identified ADAM17 variant in the pathogenesis of AD

Integrative Datenerfassung und Softwarepipelines zur Analyse multifaktorieller Krankheiten

The increasing amount of biological data available from high-throughput technologies poses great interdisciplinary challenges to research. Today, cost-efficient platforms generate manifold types of data and allow to build comprehensive resources that include but are not limited to genomics, proteomics, and metabolomics on a systemic scale. In order to adapt to this development in the post-wetlab analysis, computer scientists in computational biology work on methods and software frameworks that are able to account for data size and diversity, and allow to scrutinize data in respect to a specific context, such as the emergence of diseases. Aiming for this, we first present a desktop software framework designed to integrate biological data that features a uniform interface to perform consecutive analysis steps managed by an automated task processing system. The extensibility of the platform based on a concise plugin interface was used for a study on breast cancer for which we developed a plugin to derive gene regulatory networks. From this analysis, we derived a general approach to generate transcription factor-microRNA regulatory networks and built a webservice available for public use: TFmiR. Using differentially expressed sets of mRNAs and miRNAs, TFmiR generates a network with experimental or predicted evidence and provides downstream investigation, e.g. applying various network measures and overrepresentation analysis. Further in-depth analysis is provided with a motif search algorithm. For all motifs of particular interest, the software allows to investigate co-regulated and co-targeted subnetworks and calculates the functional similarity scores of the participating genes. We investigated a comprehensive dataset on Alzheimer's disease that was provided by the neurological laboratory in Homburg. We conducted the individual analysis of the various types of data, followed by applying our approaches to build regulatory networks, and search for potential key drivers of the Alzheimer's disease. Moreover, we show a different strategy based on patient-similarity networks with the aim to find a descriptive combination of markers for AD spanning the multiple data sources.Biotechnologische Hochdurchsatzverfahren und die damit verbundene stetig anwachsende Menge an biologischen Daten stellen die Forschung vor ebenso wachsende Herausforderungen. Neue und kosteneffiziente Verfahren erlauben die Erstellung umfangreicher Datenbanken, die beispielsweise das vollständige Genom, Proteom, oder Metabolom eines Organismus oder Individuums enthalten können. Informatiker, Bioinformatiker, und Biologen arbeiten daher an Methoden und Softwareumgebungen um dieser Entwicklung nachzukommen und diese Daten trotz ihres Umfangs und Vielfältigkeit einheitlich erfassen zu können. Dabei gilt besonderes Interesse der Notwendigkeit, diese Daten im Hinblick auf ihre Bedeutung in bestimmten Kontexten zu untersuchen, wie zum Beispiel im Zusammenhang mit Krankheiten. Mit diesem Ziel vor Augen zeigen wir zunächst die Softwareumgebung Mebitoo, die wir zur Integration und automatisierten Analyse von biologischen Daten entwickelten. Mit einer Erweiterung der Software zur Erstellung regulatorischer Netzwerke zeigen wir die vielfältige Einsetzbarkeit der Platform am Beispiel von Daten zu Brustkarzinomen. Aufbauend darauf entwickelten wir eine allgemeine Strategie zur Erstellung regulatorischer Netzwerke, die auf differentiell exprimierten Genen und microRNAs basiert. Wir stellten einen Webservice zur Verfügung, der durch die Einbindung verschiedener Datenbanken zu experimentell bestimmten oder in silico berechneten mutmaßlichen Interaktionen ein regulatorisches Netzwerk, wahlweise im Hinblick auf eine mögliche Krankheit, erstellt und untersucht. Die bereitgestellten Analysen umfassen Methoden zur generellen Netzwerkevaluierung, sowie aufwändigere Algorithmen zur Bestimmung von Netzwerkmotiven und deren Subnetzen, und die Untersuchung auf deren Funktionalität. Abschließend beschreiben wir die Untersuchung eines umfassenden Datensatzes zur Alzheimer'schen Krankheit, welcher vom neurologischen Labor der Universitätsklinik des Saarlandes zusammengestellt wurde. Die Daten umfassen Gen- und miRNA Expressionsprofile, Methylierung, Proteinlevelmessungen, und SNPs zu einer Kohorte von Alzheimerpatienten und Kontrollen. Wir untersuchten die Daten jeweils individuell und zeigten anschließend die Anwendung unserer Pipeline Identifikation von mutmaßlichen Key Drivern. Darüberhinaus verfolgten wir einen Ansatz, der auf Ähnlichkeitsnetzen für die jeweiligen Patienten beruht

Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost

Rare ABCA7 variants in 2 German families with Alzheimer disease

Objective The aim of this study was to identify variants associated with familial late-onset Alzheimer disease (AD) using whole-genome sequencing. Methods Several families with an autosomal dominant inheritance pattern of AD were analyzed by whole-genome sequencing. Variants were prioritized for rare, likely pathogenic variants in genes already known to be associated with AD and confirmed by Sanger sequencing using standard protocols. Results We identified 2 rare ABCA7 variants (rs143718918 and rs538591288) with varying penetrance in 2 independent German AD families, respectively. The single nucleotide variant (SNV) rs143718918 causes a missense mutation, and the deletion rs538591288 causes a frameshift mutation of ABCA7. Both variants have previously been reported in larger cohorts but with incomplete segregation information. ABCA7 is one of more than 20 AD risk loci that have so far been identified by genome-wide association studies, and both common and rare variants of ABCA7 have previously been described in different populations with higher frequencies in AD cases than in controls and varying penetrance. Furthermore, ABCA7 is known to be involved in several AD-relevant pathways. Conclusions We conclude that both SNVs might contribute to the development of AD in the examined family members. Together with previous findings, our data confirm ABCA7 as one of the most relevant AD risk genes

Linking Hematopoietic Differentiation to Co-Expressed Sets of Pluripotency-Associated and Imprinted Genes and to Regulatory microRNA-Transcription Factor Motifs

<div><p>Maintenance of cell pluripotency, differentiation, and reprogramming are regulated by complex gene regulatory networks (GRNs) including monoallelically-expressed imprinted genes. Besides transcriptional control, epigenetic modifications and microRNAs contribute to cellular differentiation. As a model system for studying the capacity of cells to preserve their pluripotency state and the onset of differentiation and subsequent specialization, murine hematopoiesis was used and compared to embryonic stem cells (ESCs) as a control. Using published microarray data, the expression profiles of two sets of genes, pluripotent and imprinted, were compared to a third set of known hematopoietic genes. We found that more than half of the pluripotent and imprinted genes are clearly upregulated in ESCs but subsequently repressed during hematopoiesis. The remaining genes were either upregulated in hematopoietic progenitors or in differentiated blood cells. The three gene sets each consist of three similarly behaving gene groups with similar expression profiles in various lineages of the hematopoietic system as well as in ESCs. To explain this co-regulation behavior, we explored the transcriptional and post-transcriptional mechanisms of pluripotent and imprinted genes and their regulator/target miRNAs in six different hematopoietic lineages. Therewith, lineage-specific transcription factor (TF)-miRNA regulatory networks were generated and their topologies and functional impacts during hematopoiesis were analyzed. This led to the identification of TF-miRNA co-regulatory motifs, for which we validated the contribution to the cellular development of the corresponding lineage in terms of statistical significance and relevance to biological evidence. This analysis also identified key miRNAs and TFs/genes that might play important roles in the derived lineage networks. These molecular associations suggest new aspects of the cellular regulation of the onset of cellular differentiation and during hematopoiesis involving, on one hand, pluripotent genes that were previously not discussed in the context of hematopoiesis and, on the other hand, involve genes that are related to genomic imprinting. These are new links between hematopoiesis and cellular differentiation and the important field of epigenetic modifications.</p></div

Selected hematopoietic lineages and their stages of sequential cell development.

<p>Selected hematopoietic lineages and their stages of sequential cell development.</p

Linking Hematopoietic Differentiation to Co-Expressed Sets of Pluripotency-Associated and Imprinted Genes and to Regulatory microRNA-Transcription Factor Motifs - Sheet 3

<p>Linking Hematopoietic Differentiation to Co-Expressed Sets of Pluripotency-Associated and Imprinted Genes and to Regulatory microRNA-Transcription Factor Motifs</p> - Sheet

Heatmaps showing transient changes in expression profiles.

<p>Different groups of ESC and hematopoietic cells (<i>e</i>.<i>g</i> stem cells, intermediate progenitors, and terminally differentiated blood cells) from the GSE10246 dataset for (left panel) imprinted genes, (middle panel) pluripotent genes and (right panel) hematopoietic genes were compared. Green spots represent downregulated genes, and red spots represent upregulated genes. The order of genes is obtained by hierarchical clustering, which shows three similar pattern classes between imprinted, pluripotent and hematopoietic genes.</p