Effects of the 2014 major Baltic inflow on methane and nitrous oxide dynamics in the water column of the Central Baltic Sea

Peer reviewe

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin

Prion-like alpha-synuclein pathology in the brain of infants with Krabbe disease

Pentti Tienari konsortion jäsenenäKrabbe disease is an infantile neurodegenerative disorder resulting from pathogenic variants in the GALC gene that causes accumulation of the toxic sphingolipid psychosine. GALC variants are also associated with Lewy body diseases, an umbrella term for age-associated neurodegenerative diseases in which the protein a-synuclein aggregates into Lewy bodies. To explore whether alpha-synuclein in Krabbe disease has pathological similarities to that in Lewy body disease, we performed an observational post-mortem study of Krabbe disease brain tissue (n = 4) compared to infant controls (n = 4) and identified widespread accumulations of alpha-synuclein. To determine whether alpha-synuclein in Krabbe disease brain displayed disease-associated pathogenic properties we evaluated its seeding capacity using the real-time quaking-induced conversion assay in two cases for which frozen tissue was available and strikingly identified aggregation into fibrils similar to those observed in Lewy body disease, confirming the prion-like capacity of Krabbe disease-derived alpha-synuclein. These observations constitute the first report of prion-like alpha-synuclein in the brain tissue of infants and challenge the putative view that alpha-synuclein pathology is merely an age-associated phenomenon, instead suggesting it results from alterations to biological pathways, such as sphingolipid metabolism. Our findings have important implications for understanding the mechanisms underlying Lewy body formation in Lewy body disease.Peer reviewe

CanGEM: mining gene copy number changes in cancer

The use of genome-wide and high-throughput screening methods on large sample sizes is a well-grounded approach when studying a process as complex and heterogeneous as tumorigenesis. Gene copy number changes are one of the main mechanisms causing cancerous alterations in gene expression and can be detected using array comparative genomic hybridization (aCGH). Microarrays are well suited for the integrative systems biology approach, but none of the existing microarray databases is focusing on copy number changes. We present here CanGEM (Cancer GEnome Mine), which is a public, web-based database for storing quantitative microarray data and relevant metadata about the measurements and samples. CanGEM supports the MIAME standard and in addition, stores clinical information using standardized controlled vocabularies whenever possible. Microarray probes are re-annotated with their physical coordinates in the human genome and aCGH data is analyzed to yield gene-specific copy numbers. Users can build custom datasets by querying for specific clinical sample characteristics or copy number changes of individual genes. Aberration frequencies can be calculated for these datasets, and the data can be visualized on the human genome map with gene annotations. Furthermore, the original data files are available for more detailed analysis. The CanGEM database can be accessed at http://www.cangem.org/

From clear lakes to murky waters - tracing the functional response of high-latitude lake communities to concurrent 'greening' and 'browning'

Climate change and the intensification of land use practices are causing widespread eutrophication of subarctic lakes. The implications of this rapid change for lake ecosystem function remain poorly understood. To assess how freshwater communities respond to such profound changes in their habitat and resource availability, we conducted a space-for-time analysis of food-web structure in 30 lakes situated across a temperature-productivity gradient equivalent to the predicted future climate of subarctic Europe (temperature +3 degrees C, precipitation +30% and nutrient +45 mu g L-1 total phosphorus). Along this gradient, we observed an increase in the assimilation of pelagic-derived carbon from 25 to 75% throughout primary, secondary and tertiary consumers. This shift was overwhelmingly driven by the consumption of pelagic detritus by benthic primary consumers and was not accompanied by increased pelagic foraging by higher trophic level consumers. Our data also revealed a convergence of the carbon isotope ratios of pelagic and benthic food web endmembers in the warmest, most productive lakes indicating that the incorporation of terrestrial derived carbon into aquatic food webs increases as land use intensifies. These results, reflecting changes along a gradient characteristic of the predicted future environment throughout the subarctic, indicate that climate and land use driven eutrophication and browning are radically altering the function and fuelling of aquatic food webs in this biome.Peer reviewe

Comparative genomics and genome biology of invasive Campylobacter jejuni

Campylobacter jejuni is a major pathogen in bacterial gastroenteritis worldwide and can cause bacteremia in severe cases. C. jejuni is highly structured into clonal lineages of which the ST677CC lineage has been overrepresented among C. jejuni isolates derived from blood. In this study, we characterized the genomes of 31 C. jejuni blood isolates and 24 faecal isolates belonging to ST677CC in order to study the genome biology related to C. jejuni invasiveness. We combined the genome analyses with phenotypical evidence on serum resistance which was associated with phase variation of wcbK; a GDP-mannose 4,6-dehydratase involved in capsular biosynthesis. We also describe the finding of a Type III restriction-modification system unique to the ST-794 sublineage. However, features previously considered to be related to pathogenesis of C. jejuni were either absent or disrupted among our strains. Our results refine the role of capsule features associated with invasive disease and accentuate the possibility of methylation and restriction enzymes in the potential of C. jejuni to establish invasive infections. Our findings underline the importance of studying clinically relevant well-characterized bacterial strains in order to understand pathogenesis mechanisms important in human infections.Peer reviewe

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

Real-time PCR-based assay to quantify the relative amount of human and mouse tissue present in tumor xenografts

<p>Abstract</p> <p>Background</p> <p>Xenograft samples used to test anti-cancer drug efficacies and toxicities in vivo contain an unknown mix of mouse and human cells. Evaluation of drug activity can be confounded by samples containing large amounts of contaminating mouse tissue. We have developed a real-time quantitative polymerase chain reaction (qPCR) assay using TaqMan technology to quantify the amount of mouse tissue that is incorporated into human xenograft samples.</p> <p>Results</p> <p>The forward and reverse primers bind to the same DNA sequence in the human and the mouse genome. Using a set of specially designed fluorescent probes provides species specificity. The linearity and sensitivity of the assay is evaluated using serial dilutions of single species and heterogeneous DNA mixtures. We examined many xenograft samples at various in vivo passages, finding a wide variety of human:mouse DNA ratios. This variation may be influenced by tumor type, number of serial passages in vivo, and even which part of the tumor was collected and used in the assay.</p> <p>Conclusions</p> <p>This novel assay provides an accurate quantitative assessment of human and mouse content in xenograft tumors. This assay can be performed on aberrantly behaving human xenografts, samples used in bioinformatics studies, and periodically for tumor tissue frequently grown by serial passage in vivo.</p

CDCOCA: a statistical method to define complexity dependent co-occurring chromosomal aberrations

<p>Abstract</p> <p>Background</p> <p>Copy number alterations (CNA) play a key role in cancer development and progression. Since more than one CNA can be detected in most tumors, frequently co-occurring genetic CNA may point to cooperating cancer related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNA per tumor, resulting in a preferential detection of frequent changes with limited specificity for each association due to the high genetic instability of many samples.</p> <p>Method</p> <p>We hypothesize that in cancer some linkage-independent CNA may display a non-random co-occurrence, and that these CNA could be of pathogenetic relevance for the respective cancer. We also hypothesize that the statistical relevance of co-occurring CNA may depend on the sample specific CNA complexity. We verify our hypotheses with a simulation based algorithm CDCOCA (complexity dependence of co-occurring chromosomal aberrations).</p> <p>Results</p> <p>Application of CDCOCA to example data sets identified co-occurring CNA from low complex background which otherwise went unnoticed. Identification of cancer associated genes in these co-occurring changes can provide insights of cooperative genes involved in oncogenesis.</p> <p>Conclusions</p> <p>We have developed a method to detect associations of regional copy number abnormalities in cancer data. Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.</p