Epidemiologie, Bedeutung und integrierte Bekämpfungsmöglichkeiten von Leptosphaeria maculans (Phoma lingam) sowie weiterer pilzlicher Krankheitserreger im schleswig-holsteinischen Winterrapsanbau (Brassica napus L. var. napus)

The aim of this investigation was to record the appearance and epidemiology of Leptosphaeria maculans (Phoma lingam) and other winter oilseed rape pathogens over the years 2000-2003 under the production and weather conditions of Schleswig-Holstein. The studies included the recording of weather data, the determination of L. maculans ascospore flight by using a Burkhard spore trap and epidemiological studies in untreated plots and in plots with different fungicide treatments. Under the aspect of integrated pest management additional fungicide applications which were more orientated on the epidemiology of P. lingam were also tested in these trials regarding their potential for optimal pest and yield control. Two stem-rot (Sclerotinia sclerotiorum (Lib.) de Bary) forecast systems were examined and a winter oilseed rape cultivar was tested regarding genetic effects on pathogen progressions. The results emphasised close relationship between precipitation and ascospore release from L. maculans, ascospore flight and leaf infection by P. lingam and also between the autumn infection of the leaves and root collar infection. Leaf infection by P. lingam was reduced after a fungicide application in autumn as well as in spring. An effective control of P. lingam on the root collar resulted only through autumn treatment. The highest yield effects resulted of the application at flowering and spring while an increased yield after autumn treatment could only be measured in the presence of high Phoma lingam infection incidences and severities. A higher incidence of Sclerotinia stem rot could only be detected in the trial year 2001. The appearance of Verticillium dahliae was observed at every trial site and every year, while pathogen incidence increased with increasing proportions of oilseed rape in the rotation

Feature Selection for Predicting Tumor Metastases in Microarray Experiments using Paired Design

Among the major issues in gene expression profile classification, feature selection is an important and necessary step in achieving and creating good classification rules given the high dimensionality of microarray data. Although different feature selection methods have been reported, there has been no method specifically proposed for paired microarray experiments. In this paper, we introduce a simple procedure based on a modified t-statistic for feature selection to microarray experiments using the popular matched case-control design and apply to our recent study on tumor metastasis in a low-malignant group of breast cancer patients for selecting genes that best predict metastases. Gene or feature selection is optimized by thresholding in a leaving one-pair out cross-validation. Model comparison through empirical application has shown that our method manifests improved efficiency with high sensitivity and specificity

Gene Expression Meta-Analysis identifies Cytokine Pathways and 5q Aberrations involved in Metastasis of ERBB2 Amplified and Basal Breast Cancer

Background Breast tumors have been described by molecular subtypes characterized by pervasively different gene expression profiles. The subtypes are associated with different clinical parameters and origin of precursor cells. However, the biological pathways and chromosomal aberrations that differ between the subgroups are less well characterized. The molecular subtypes are associated with different risk of metastatic recurrence of the disease. Nevertheless, the performance of these overall patterns to predict outcome is far from optimal, suggesting that biological mechanisms that extend beyond the subgroups impact metastasis. Results We have scrutinized publicly available gene expression datasets and identified molecular subtypes in 1,394 breast tumors with outcome data. By analysis of chromosomal regions and pathways using “Gene set enrichment analysis” followed by a meta-analysis, we identified comprehensive mechanistic differences between the subgroups. Furthermore, the same approach was used to investigate mechanisms related to metastasis within the subgroups. A striking finding is that the molecular subtypes account for the majority of biological mechanisms associated with metastasis. However, some mechanisms, aside from the subtypes, were identified in a training set of 1,239 tumors and confirmed by survival analysis in two independent validation datasets from the same type of platform and consisting of very comparable node-negative patients that did not receive adjuvant medical therapy. The results show that high expression of 5q14 genes and low levels of TNFR2 pathway genes were associated with poor survival in basal-like cancers. Furthermore, low expression of 5q33 genes and interleukin-12 pathway genes were associated with poor outcome exclusively in ERBB2-like tumors. Conclusion The identified regions, genes, and pathways may be potential drug targets in future individualized treatment strategies

Circulating Tumor DNA in Patients with Renal Cell Carcinoma. A Systematic Review of the Literature

CONTEXT: Over the past decade there has been increasing interest in the potential of liquid biopsies and systematic biomarkers in the diagnosis and management of kidney cancer, as they may provide a tool for early detection of disease and monitoring of treatment response. OBJECTIVE: To identify and summarize relevant published data on circulating tumor DNA (ctDNA) in patients with renal cell carcinoma (RCC). EVIDENCE ACQUISITION: We performed a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement of studies identified in PubMed, MEDLINE, EMBASE, and Cochrane Library up to January 15, 2021. Two reviewers independently screened all articles and performed the data extraction. EVIDENCE SYNTHESIS: Nineteen studies investigating ctDNA in RCC (1237 patients) were included and analyzed in the final review. The study size and design varied widely, and the studies were divided into five groups according to the method used for ctDNA detection. The outcome data included (1) the sensitivity/specificity if available; (2) the method used for ctDNA detection; and (3) the main findings in the studies. CONCLUSIONS: The studies highlight that the level of ctDNA in RCC appears to be low. Studies using multiple methods for ctDNA detection indicate that tumor-guided analysis improves the ctDNA detection rate and suggest that cell-free methylated DNA immunoprecipitation and high-throughput sequencing may be a very sensitive method for ctDNA detection in RCC. PATIENT SUMMARY: We systematically reviewed the literature to identify all relevant studies investigating circulating tumor DNA in patients with kidney cancer to investigate its use and potential in this highly malignant disease. We found that the level of circulating tumor DNA is low in kidney cancer and that very sensitive methods have to be used for detection in this disease

A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances

Power estimation for gene-longevity association analysis using concordant twins.

Statistical power is one of the major concerns in genetic association studies. Related individuals such as twins are valuable samples for genetic studies because of their genetic relatedness. Phenotype similarity in twin pairs provides evidence of genetic control over the phenotype variation in a population. The genetic association study on human longevity, a complex trait that is under control of both genetic and environmental factors, has been confronted by the small sample sizes of longevity subjects which limit statistical power. Twin pairs concordant for longevity have increased probability for carrying beneficial genes and thus are useful samples for gene-longevity association analysis. We conducted a computer simulation to estimate the power of association study using longevity concordant twin pairs. We observed remarkable power increases in using singletons from longevity concordant twin pairs as cases in comparison with cases of sporadic proband. A similar power would require doubled sample sizes for fraternal twins than for identical twins who are concordant for longevity suggesting that longevity concordant identical twins are more efficient samples than fraternal twins. We also observed an approximate of 2- to 3-fold increase in sample sizes needed for longevity cutoff at age 90 as compared with that at age 95. Overall, our results showed high value of twins in genetic association studies on human longevity.Peer Reviewe

Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

<p>Abstract</p> <p>Background</p> <p>The etiology of multifactorial human diseases involves complex interactions between numerous environmental factors and alleles of many genes. Efficient statistical tools are demanded in identifying the genetic and environmental variants that affect the risk of disease development. This paper introduces a retrospective polytomous logistic regression model to measure both the main and interaction effects in genetic association studies of human discrete and continuous complex traits. In this model, combinations of genotypes at two interacting loci or of environmental exposure and genotypes at one locus are treated as nominal outcomes of which the proportions are modeled as a function of the disease trait assigning both main and interaction effects and with no assumption of normality in the trait distribution. Performance of our method in detecting interaction effect is compared with that of the case-only model.</p> <p>Results</p> <p>Results from our simulation study indicate that our retrospective model exhibits high power in capturing even relatively small effect with reasonable sample sizes. Application of our method to data from an association study on the catalase -262C/T promoter polymorphism and aging phenotypes detected significant main and interaction effects for age-group and allele T on individual's cognitive functioning and produced consistent results in estimating the interaction effect as compared with the popular case-only model.</p> <p>Conclusion</p> <p>The retrospective polytomous logistic regression model can be used as a convenient tool for assessing both main and interaction effects in genetic association studies of human multifactorial diseases involving genetic and non-genetic factors as well as categorical or continuous traits.</p

Efficient Sample Tracking With OpenLabFramework

The advance of new technologies in biomedical research has led to a dramatic growth in experimental throughput. Projects therefore steadily grow in size and involve a larger number of researchers. Spreadsheets traditionally used are thus no longer suitable for keeping track of the vast amounts of samples created and need to be replaced with state-of-the-art laboratory information management systems. Such systems have been developed in large numbers, but they are often limited to specific research domains and types of data. One domain so far neglected is the management of libraries of vector clones and genetically engineered cell lines. OpenLabFramework is a newly developed web-application for sample tracking, particularly laid out to fill this gap, but with an open architecture allowing it to be extended for other biological materials and functional data. Its sample tracking mechanism is fully customizable and aids productivity further through support for mobile devices and barcoded labels