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Variables obtained by experimental measurements or statistical inference typically carry uncertainties. When an algorithm uses such quantities as input variables, this uncertainty should propagate to the algorithm's output. Concretely, we consider the classic notion of principal component analysis (PCA): If it is applied to a finite data matrix containing imperfect (i.e., uncertain) multidimensional measurements, its output—a lower-dimensional representation—is itself subject to uncertainty. We demonstrate that this uncertainty can be approximated by appropriate linearization of the algorithm's nonlinear functionality, using automatic differentiation. By itself, however, this structured, uncertain output is difficult to interpret for users. We provide an animation method that effectively visualizes the uncertainty of the lower dimensional map. Implemented as an open-source software package, it allows researchers to assess the reliability of PCA embeddings.

ABSTRACTFK506 (tacrolimus) is a valuable immunosuppressant produced by several Streptomycesstrains. In the genome of the wild type producer Streptomyces tsukubaensisNRRL18488, FK506 biosynthesis is encoded by a gene cluster that spans 83.5 (kb). A whole transcriptome differential shotgun sequencing (dRNA-seq) of S. tsukubaensiswas performed to analyze transcription at 2 different time points; before and during active FK506 production. In total, 8,914 transcription start sites were identified in either condition, which enabled precise determination of the 5′-UTR length of the corresponding transcripts as well as the identification of 2 consensus sequence motifs in the promoter regions. The transcription start sites of all gene operons within the FK506 cluster were identified, including 3 examples of leaderless RNA transcripts. These data provide detailed insight into the transcription of the FK506 biosynthetic gene cluster to support future regulatory studies, genetic manipulation, and industrial production.

The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process. Here we introduce EAGER, a time-efficient pipeline, which greatly simplifies the analysis of large-scale genomic data sets. EAGER provides features to preprocess, map, authenticate, and assess the quality of ancient DNA samples. Additionally, EAGER comprises tools to genotype samples to discover, filter, and analyze variants. EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

ABSTRACTRecalcitrance of genetically susceptible bacteria to antibiotic killing is a hallmark of bacterial drug tolerance. This phenomenon is prevalent in biofilms, persisters, and also planktonic cells and is associated with chronic or relapsing infections with pathogens such as Staphylococcus aureus. Here we report the in vitroevolution of an S. aureusstrain that exhibits a high degree of nonsusceptibility to daptomycin as a result of cyclic challenges with bactericidal concentrations of the drug. This phenotype was attributed to stationary growth phase-dependent drug tolerance and was clearly distinguished from resistance. The underlying genetic basis was revealed to be an adaptive point mutation in the putative inorganic phosphate (Pi) transporter gene pitA. Drug tolerance caused by this allele, termed pitA6, was abrogated when the upstream gene pitRwas inactivated. Enhanced tolerance toward daptomycin, as well as the acyldepsipeptide antibiotic ADEP4 and various combinations of other drugs, was accompanied by elevated intracellular concentrations of Piand polyphosphate, which may reversibly interfere with critical cellular functions. The evolved strain displayed increased rates of survival within human endothelial cells, demonstrating the correlation of intracellular persistence and drug tolerance. These findings will be useful for further investigations of S. aureusdrug tolerance, toward the development of additional antipersister compounds and strategies.

The translocation t(15;17) generates the chimeric PML-RARα transcription factor that is the initiating event of acute promyelocytic leukemia. A global view of PML-RARα transcriptional functions was obtained by genome-wide binding and chromatin modification analyses combined with genome-wide expression data. Chromatin immunoprecipitation (ChIP)–chip experiments identified 372 direct genomic PML-RARα targets. A subset of these was confirmed in primary acute promyelocytic leukemia. Direct PML-RARα targets include regulators of global transcriptional programs as well as critical regulatory genes for basic cellular functions such as cell-cycle control and apoptosis. PML-RARα binding universally led to HDAC1 recruitment, loss of histone H3 acetylation, increased tri-methylation of histone H3 lysine 9, and unexpectedly increased trimethylation of histone H3 lysine 4. The binding of PML-RARα to target promoters and the resulting histone modifications resulted in mRNA repression of functionally relevant genes. Taken together, our results reveal that the transcription factor PML-RARα regulates key cancer-related genes and pathways by inducing a repressed chromatin formation on its direct genomic target genes.

The translocation t(15;17) generates the chimeric PML-RARα transcription factor that is the initiating event of acute promyelocytic leukemia. A global view of PML-RARα transcriptional functions was obtained by genome-wide binding and chromatin modification analyses combined with genome-wide expression data. Chromatin immunoprecipitation (ChIP)–chip experiments identified 372 direct genomic PML-RARα targets. A subset of these was confirmed in primary acute promyelocytic leukemia. Direct PML-RARα targets include regulators of global transcriptional programs as well as critical regulatory genes for basic cellular functions such as cell-cycle control and apoptosis. PML-RARα binding universally led to HDAC1 recruitment, loss of histone H3 acetylation, increased tri-methylation of histone H3 lysine 9, and unexpectedly increased trimethylation of histone H3 lysine 4. The binding of PML-RARα to target promoters and the resulting histone modifications resulted in mRNA repression of functionally relevant genes. Taken together, our results reveal that the transcription factor PML-RARα regulates key cancer-related genes and pathways by inducing a repressed chromatin formation on its direct genomic target genes.

It is generally accepted that exhausting endurance exercise exhibits strong effects on the immune system. Such effects have been attributed to changes in the cellular composition of peripheral blood as well as to changes in the expression of plausible candidate genes. The list of candidate genes is far from being complete, since this issue has not yet been investigated in a systematic way. In this study, we used a custom-made cDNA microarray focused on inflammation as a screening approach to study gene expression in eight one-half marathon runners before, immediately after, and 24 h after exercise. Significant differential gene expression was verified by quantitative real-time PCR. Linear regression analysis showed that microarray expression analysis of cell type-specific surface molecules reflects the observed individual cellular shifts in peripheral blood cells with high statistical significance. In line with the results of former studies, we observed an upregulation of mitogen-activated protein kinase-activated protein kinase-2 (MAPKAP-K2), l-selectin, and IL-1 receptor antagonist (IL-1ra) after exhaustive exercise. The main results of this study report, for the first time, the downregulation of CD81; the upregulation of thioredoxin, which may play an important part in anti-oxidative defense; and, surprisingly, the downregulation of the anti-carcinogenic gene glutathione-S-transferase-3 (GSTM3) in peripheral blood. The study shows cDNA microarray expression analysis as a reliable systematic instrument to complete the list of candidate genes that may play a role in exhaustive exercise-induced modulation of the immune response.

We have developed a methodology that allows integration of microarray data and meta information within a visualization in order to guide the investigator during data exploration and analysis. A simple mathematical framework is introduced that uses scoring functions to map meta information to relevance ratings of genes. To explore the potential of this framework we extended the traditional heatmap with new features to graphically represent the relevance ratings. These ratings are visualized by an additional color gradient, by scaling the vertical height of matrix rows, by rearranging rows or by inserting new columns into the heatmap. This visualization is called an enhanced heatmap.We have applied our approach to microarray data of the Saccharomyces cerevisiaecell cycle, complemented with supplemental data that we both derived from the microarray data itself and retrieved from public databases. Using these data we demonstrate how this visualization concept can be efficiently used to identify certain features of genes and to detect inconsistencies in the data. Thus, the investigator has the possibility to get an overview of data from various sources and at the same time can gain a deeper insight into the structure of the combined data. The concept is not restricted to heatmaps, and can be used to extend further visualization techniques, such as profile plots. We found that our method is a powerful tool to integrate supplemental data into microarray visualizations and that it increases the efficiency of visual data exploration, which is a fundamental part of microarray data analyses.

Bacteria in the genus Streptomyces are soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolor M145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoP mutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures.

Bacteria in the genus Streptomycesare soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolorM145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolormutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoPmutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures.

Aberrant transcriptional regulation plays a crucial role in the pathogenesis of acute leukemias. Chromosomal translocations and other mutations frequently affect transcription and chromatin regulation-associated genes in these diseases. Based on these findings we hypothesized that distinct global chromatin modification patterns exist that can distinguish between progenitor cells (CD34+ HSC) and acute leukemia as well as its subtypes. We used high density oligonucleotide ChIP-Chip assays querying more than 31,000 genomic loci to analyze global Histone H3 acetylation (H3Ac) and Lysine 9 (H3K9me3) trimethylation patterns each in a large number of AML (n=115), ALL (n=30), CD34+ HSC (n= 21) and peripheral blood cell (N=18) specimens. Class comparisons and predictions as well as unsupervised analyses were performed. Bioinformatic analyses led to histone modification maps across the genome with Histone H3 acetylation levels peaking around the predicted transcriptional start sites. More than 1000 loci differed in H3 acetylation and H3K9me3 between AML and CD34+ specimens (5% FDR). Among the regulatory classes over-represented among the altered genes were those involved in oncogenesis and cellular proliferation/differentiation. Histone H3 acetylation and H3K9me3 patterns also differed at hundreds of loci between ALL and AML samples (5% FDR). Genes involved in transcriptional regulation were significantly altered between the two leukemia subtypes. Specific differences in global chromatin modifications allowed support vector machine-based classification of CD34+ progenitor cells, AML, and ALL, with about 90% sensitivity and specificity based on Histone H3 acetylation patterns. A similar classification power was observed for H3K9me3. Within the AML patients, several groups of patients were identified that clustered together in unsupervised hierarchical cluster analysis due to similar histone modification patterns. These patterns did not primarily depend on patients′ karyotypes,, indicating that it may be possible to define some, as yet unknown, different types of AML based on chromatin modification. On a global scale, acute leukemias are associated with specific histone modification patterns that distinguish AML from ALL and CD34+ hematopoietic progenitors. Taken together, these first data on the leukemia epigenome provide the basis for improved understanding of genes involved in leukemia pathogenesis.

Aberrant transcriptional regulation plays a crucial role in the pathogenesis of acute leukemias. Chromosomal translocations and other mutations frequently affect transcription and chromatin regulation-associated genes in these diseases. Based on these findings we hypothesized that distinct global chromatin modification patterns exist that can distinguish between progenitor cells (CD34+ HSC) and acute leukemia as well as its subtypes. We used high density oligonucleotide ChIP-Chip assays querying more than 31,000 genomic loci to analyze global Histone H3 acetylation (H3Ac) and Lysine 9 (H3K9me3) trimethylation patterns each in a large number of AML (n=115), ALL (n=30), CD34+ HSC (n= 21) and peripheral blood cell (N=18) specimens. Class comparisons and predictions as well as unsupervised analyses were performed. Bioinformatic analyses led to histone modification maps across the genome with Histone H3 acetylation levels peaking around the predicted transcriptional start sites. More than 1000 loci differed in H3 acetylation and H3K9me3 between AML and CD34+ specimens (5% FDR). Among the regulatory classes over-represented among the altered genes were those involved in oncogenesis and cellular proliferation/differentiation. Histone H3 acetylation and H3K9me3 patterns also differed at hundreds of loci between ALL and AML samples (5% FDR). Genes involved in transcriptional regulation were significantly altered between the two leukemia subtypes. Specific differences in global chromatin modifications allowed support vector machine-based classification of CD34+ progenitor cells, AML, and ALL, with about 90% sensitivity and specificity based on Histone H3 acetylation patterns. A similar classification power was observed for H3K9me3. Within the AML patients, several groups of patients were identified that clustered together in unsupervised hierarchical cluster analysis due to similar histone modification patterns. These patterns did not primarily depend on patients′ karyotypes, indicating that it may be possible to define some, as yet unknown, different types of AML based on chromatin modification. On a global scale, acute leukemias are associated with specific histone modification patterns that distinguish AML from ALL and CD34+ hematopoietic progenitors. Taken together, these first data on the leukemia epigenome provide the basis for improved understanding of genes involved in leukemia pathogenesis.

Mayday is a workbench for visualization, analysis and storage of microarray data. It features a graphical user interface and supports the development and integration of existing and new analysis methods. Besides the infrastructural core functionality, Mayday offers a variety of plug-ins, such as various interactive viewers, a connection to the R statistical environment, a connection to SQL-based databases and different data mining methods, including WEKA-library based methods for classification and various clustering methods. In addition, so-called meta information objects are provided for annotation of the microarray data allowing integration of data from different sources, which is a feature that, for instance, is employed in the enhanced heatmap visualization. Contact: nieselt@informatik.uni-tuebingen.de Supplementary information: The software and more detailed information including screenshots and a user guide as well as test data can be found on the Mayday home page http://www.zbit.uni-tuebingen.de/pas/mayday. The core is published under the GPL (GNU Public License) and the associated plug-ins under the LGPL (Lesser GNU Public License).