570 Biowissenschaften; Biologie
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Background
Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student’s t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive.
Results
By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection.
Conclusions
With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.
To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License.
Prostate cancer (PCa) is one amongst the most common cancersin western men. Incidence rate ofPCa is on the rise worldwide. The present study deals with theserum lipidome profiling of patients diagnosed with PCa to identify potential new biomarkers. We employed ESI-MS/MS and GC-MS for identification of significantly altered lipids in cancer patient’s serum compared to controls. Lipidomic data revealed 24 lipids are significantly altered in cancer patinet’s serum (n = 18) compared to normal (n = 18) with no history of PCa. By using hierarchical clustering and principal component analysis (PCA) we could clearly separate cancer patients from control group. Correlation and partition analysis along with Formal Concept Analysis (FCA) have identified that PC (39:6) and FA (22:3) could classify samples with higher certainty. Both the lipids, PC (39:6) and FA (22:3) could influence the cataloging of patients with 100% sensitivity (all 18 control samples are classified correctly) and 77.7% specificity (of 18 tumor samples 4 samples are misclassified) with p-value of 1.612×10−6 in Fischer’s exact test. Further, we performed GC-MS to denote fatty acids altered in PCa patients and found that alpha-linolenic acid (ALA) levels are altered in PCa. We also performed an in vitro proliferation assay to determine the effect of ALA in survival of classical human PCa cell lines LNCaP and PC3. We hereby report that the altered lipids PC (39:6) and FA (22:3) offer a new set of biomarkers in addition to the existing diagnostic tests that could significantly improve sensitivity and specificity in PCa diagnosis.
Gegenstand dieser Veröfffentlichung ist eine Reimplementierung des TSNI (Time Series Network Identification)-Tools zur Rekonstruktion von genregulatorischen Netzwerken aus Zeitserien von Gen-Expressionsprofilen. Das auf gewöhnlichen Differentialgleichungen basierende Tool wurde algorithmisch verbessert und an die Verarbeitung großer Datensätze angepasst. Durch Ausschöpfung des Potentials moderner Multi-CPU und GPU-Architekturen wurden die erforderlichen Berechnungen erheblich beschleunigt. Bei der Überführung des mit Matlab implementierten Tools in die Programmiersprache C wurde zudem die Bedienbarkeit verbessert.
Durch Einsatz der GPU-beschleunigten Mathematik-Bibliothek MAGMA konnten unter Verwendung eines normalen Desktop-Rechners Leistungssteigerungen um das 18,7-fache bei 11.000 Genen verzeichnet werden. Darüber hinaus rekonstruierte das verbesserte TSNI weitaus mehr biologisch richtige Beziehungen als vergleichbare Anwendungen.
Background
Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients.
Methods
Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways.
Results
It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674).
Conclusion
With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.
Genomweite Identifizierung von chromosomalen Bruchpunkten bei Tumoren unterschiedlicher Gewebe
(2016)
Aufgrund der jährlich ansteigenden Anzahl an Krebsneuerkrankungen und der tödlichen Verläufe von malignen Tumoren gewinnt die vollständige Aufklärung der Tumorgenese und -progression immer mehr an Bedeutung. Für diese können Untersuchungen zu Bruchpunkten, die eine Kopienzahlvariation (CNV) in Krebsgenomen bewirken, genutzt werden. Es wurde eine Pipeline entwickelt, die in der Lage ist, CNVs, Bruchpunktregionen (BPRs) und Gene genomweit mit Hilfe von SNP-Array-Daten zu detektieren. Dazu wurden 2.820 Tumorproben aus 8 Tumorentitäten untersucht und mit 432 Proben aus gesundem Gewebe verglichen. In den Tumorproben wurden vierfach mehr BPRs detektiert, wobei unter 5 % der Gene in den Normalproben betroffen sind. Wir identifizierten 31 hochspezifische BPRs. Die am häufigsten vorkommende Variation umschließt das Gen KIAA0513, welches in Verbindung mit der Apoptose steht. Anhand der hier entwickelten Pipeline können erste Einblicke in CNV- und Bruchpunkt-Muster in Tumorgenomen gewonnen werden, die zu einem verbesserten Verständnis der Tumorgenese und somit zu einer verbesserten Diagnostik und Therapie von Krebserkrankungen führen können.
The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data.
We have re-analyzed previously published gene expression data from ninety-four pancreatic ductal adenocarcinomas (PDAC) samples. We determined the gene expression profile of genes differentially expressed in PDAC compared to non-malignant pancreatic tissue. Using the 100 top-ranked genes, we were able to discriminate between PDAC and non-malignant pancreatic tissue. A hierarchical cluster analysis revealed only a 6 % false discovery rate. The prognostic strength of these discriminative genes was underscored by a SVM classification and 3-fold cross validation with an 89 % correct class assignment. The annotation of the 100 top-ranked genes revealed that most of the genes were involved in the processes of signal transduction, cell adhesion, extracellular matrix organization and cell migration. The most greatly affected signal cascade was the transforming growth factor β receptor signaling pathway, which was significantly enriched in the top-ranked genes. Furthermore, we identified eleven genes that were associated with good prognosis.
The importance of software validation increases since the need for high usability and suitability of software applications grows. In order to reduce costs and manage risk factors, more and more recommendations and rules have been established. In the field of pharmacy the vendors of so-called chromatography data systems (CDSs) had to implement the guidelines of the Code of Federal Regulations Title 21 (CFR 21) during the last few years in order to fulfill the increasing requirements. The CFR 21 part 11 deals with electronic records and signatures. This part is binding for each company in the regulated environment that wishes to create, edit and sign electronic information instead of printing them on paper. Subsection CFR 21 part 11.10(h) explains how to perform an input check for manual user entries as well as for data that will be collected from an external device. In this article we present an approach performing the double entry method on data provided by the hardware instrument in order to investigate possible influences on the raw data by the handling CDS.
A software tool has been written which allows us to communicate with a high-performance liquid chromatography (HPLC) detector and acquire data from it. The communication is completely independent of a CDS which is started separately and connected to the same system. Using this configuration we made a parallel data acquisition of two instances at the same time possible. Two CDSs have been tested and for at least one of them it has been shown that a comparison of the acquired data can be done as with the double entry method for the data verification. For the second CDS we checked whether it would be applicable after a few modifications. The given approach could be either used for a live data verification of produced raw data or as a single test during a software operational qualification to verify the data acquisition functionality of the software.
Carcinogenesis is a complex multifactorial, multistage process, but the precise mechanisms are not well understood. In this study, we performed a genome-wide analysis of the copy number variation (CNV), breakpoint region (BPR) and fragile sites in 2,737 tumor samples from eight tumor entities and in 432 normal samples. CNV detection and BPR identification revealed that BPRs tended to accumulate in specific genomic regions in tumor samples whereas being dispersed genome-wide in the normal samples. Hotspots were observed, at which segments with similar alteration in copy number were overlapped along with BPRs adjacently clustered. Evaluation of BPR occurrence frequency showed that at least one was detected in about and more than 15% of samples for each tumor entity while BPRs were maximal in 12% of the normal samples. 127 of 2,716 tumor-relevant BPRs (termed ‘common BPRs’) exhibited also a noticeable occurrence frequency in the normal samples. Colocalization assessment identified 20,077 CNV-affecting genes and 169 of these being known tumor-related genes. The most noteworthy genes are KIAA0513 important for immunologic, synaptic and apoptotic signal pathways, intergenic non-coding RNA RP11-115C21.2 possibly acting as oncogene or tumor suppressor by changing the structure of chromatin, and ADAM32 likely importance in cancer cell proliferation and progression by ectodomain-shedding of diverse growth factors, and the well-known tumor suppressor gene p53. The BPR distributions indicate that CNV mutations are likely non-random in tumor genomes. The marked recurrence of BPRs at specific regions supports common progression mechanisms in tumors. The presence of hotspots together with common BPRs, despite its small group size, imply a relation between fragile sites and cancer-gene alteration. Our data further suggest that both protein-coding and non-coding genes possessing a range of biological functions might play a causative or functional role in tumor biology. This research enhances our understanding of the mechanisms for tumorigenesis and progression.