The filamentous fungus Aspergillus niger is widely exploited by the fermentation industry for the production of enzymes and organic acids, particularly citric acid. We sequenced the 33.9-megabase genome of A. niger CBS 513.88, the ancestor of currently used enzyme production strains. A high level of synteny was observed with other aspergilli sequenced. Strong function predictions were made for 6,506 of the 14,165 open reading frames identified. A detailed description of the components of the protein secretion pathway was made and striking differences in the hydrolytic enzyme spectra of aspergilli were observed. A reconstructed metabolic network comprising 1,069 unique reactions illustrates the versatile metabolism of A. niger. Noteworthy is the large number of major facilitator superfamily transporters and fungal zinc binuclear cluster transcription factors, and the presence of putative gene clusters for fumonisin and ochratoxin A synthesis.

A new, multi-threaded version of the GC–MS and LC–MS data processing software, metAlign, has been developed which is able to utilize multiple cores on one PC. This new version was tested using three different multi-core PCs with different operating systems. The performance of noise reduction, baseline correction and peak-picking was 8–19 fold faster compared to the previous version on a single core machine from 2008. The alignment was 5–10 fold faster. Factors influencing the performance enhancement are discussed. Our observations show that performance scales with the increase in processor core numbers we currently see in consumer PC hardware development.

As a soil fungus, Aspergillus niger can metabolize a wide variety of carbon sources, employing sets of enzymes able to degrade plant-derived polysaccharides. In this study the genome sequence of A. niger strain CBS 513.88 was surveyed, to analyse the gene/enzyme network involved in utilization of the plant storage polymer inulin, and of sucrose, the substrate for inulin synthesis in plants. In addition to three known activities, encoded by the genes suc1 (invertase activity; designated sucA), inuE (exo-inulinase activity) and inuA/inuB (endo-inulinase activity), two new putative invertase-like proteins were identified. These two putative proteins lack N-terminal signal sequences and therefore are expected to be intracellular enzymes. One of these two genes, designated sucB, is expressed at a low level, and its expression is up-regulated when A. niger is grown on sucrose-or inulin-containing media. Transcriptional analysis of the genes encoding the sucrose-(sucA) and inulin-hydrolysing enzymes (inuA and inuE) indicated that they are similarly regulated and all strongly induced on sucrose and inulin. Analysis of a DcreA mutant strain of A. niger revealed that expression of the extracellular inulinolytic enzymes is under control of the catabolite repressor CreA. Expression of the inulinolytic enzymes was not induced by fructose, not even in the DcreA background, indicating that fructose did not act as an inducer. Evidence is provided that sucrose, or a sucrose-derived intermediate, but not fructose, acts as an inducer for the expression of inulinolytic genes in A. niger.

SummaryThe rapid increase of ~omics datasets generated by microarray, mass spectrometry and next generation sequencing technologies requires an integrated platform that can combine results from different ~omics datasets to provide novel insights in the understanding of biological systems. MADMAX is designed to provide a solution for storage and analysis of complex ~omics datasets. In addition, analysis results (such as lists of genes) can be merged to reveal candidate genes supported by all datasets. The system constitutes an ISA-Tab compliant LIMS part, which is linked to the different analysis pipelines. A pilot study of different type of ~omics data in Brassica rapa demonstrates the possible use of MADMAX. The web-based user interface provides easy access to data and analysis tools on top of the database. IntroductionTo better understand how phenotypes emerge, increasingly series of ~omics technologies (genomics, transcriptomics, proteomics, metabolomics) rather than individual measurements are necessarily used within a single study. Such efforts boost the demands of both data storage and data analysis of different high-throughput approaches. However, in the past it was hardly possible to store metadata from different ~omics technologies in the same repository. To accommodate this demand the ISA-Tab [1] format was proposed to build up a common structured representation of the metadata of studies from a combination of technologies. This also triggered attempts to develop data processing tools tailored to the needs of biologists. Unfortunately most of these tools have high demands on hardware requirements, or contain non-intuitive command line-based interfaces.* To whom correspondence should be addressed: jack.leunissen@wur.nl Here we present MADMAX, a multi-purpose database for the management and analysis of data from multiple ~omics experiments. It includes an ISA-Tab compliant backend database and a series of analysis pipelines for transcriptomics, metabolomics and genomics datasets; these pipelines are connected to the database through webservices such that other pipelines can be easily integrated into the current system ( Figure 1 Through the web interface, the user can store a complete experiment with all fields required in ISA-Tab format, sufficient to allow for subsequent analysis or even repeating the experiment later. Another section on the website is the central access to different analysis pipelines. Both individual analysis results and combined gene lists can be retrieved in the system for download. Centrally stored experiments and analysis results can only be accessed by the creator by default and will be accessible for other users only if the creator desires to share the data. The system is on an automatic backup schedule.MADMAX can be reached at http://madmax2.bioinformatics.nl/ and is available upon request by sending an email to madmax.request@bioinformatics.nl. ImplementationMADMAX is built upon an Oracle relational database on a Linux server and a computational analysis engine for different...

The fungus Aspergillus niger is an industrial producer of pectin-degrading enzymes. The recent solving of the genomic sequence of A. niger allowed an inventory of the entire genome of the fungus for potential carbohydrate-degrading enzymes. By applying bioinformatics tools, 12 new genes, putatively encoding family 28 glycoside hydrolases, were identified. Seven of the newly discovered genes form a new gene group, which we show to encode exoacting pectinolytic glycoside hydrolases. This group includes four exo-polygalacturonan hydrolases (PGAX, PGXA, PGXB and PGXC) and three putative exo-rhamnogalacturonan hydrolases (RGXA, RGXB and RGXC). Biochemical identification using polygalacturonic acid and xylogalacturonan as substrates demonstrated that indeed PGXB and PGXC act as exo-polygalacturonases, whereas PGXA acts as an exo-xylogalacturonan hydrolase. The expression levels of all 21 genes were assessed by microarray analysis. The results from the present study demonstrate that exo-acting glycoside hydrolases play a prominent role in pectin degradation.

A strategy, detailed methodology description and software are given with which the mass accuracy of U-HPLC-Orbitrap data (resolving power 50,000 FWHM) can be enhanced by an order of magnitude to sub-ppm levels. After mass accuracy enhancement all 211 reference masses have mass errors within 0.5 ppm; only 14 of these are outside the 0.2 ppm error margin. Further demonstration of mass accuracy enhancement is shown on a pre-concentrated urine sample in which evidence for 89 (342 ions) potential hydroxylated and glucuronated DHEA-metabolites is found. Although most DHEA metabolites have low-intensity mass signals, only 11 out of 342 are outside the ±1 ppm error envelop; 272 mass signals have errors below 0.5 ppm (142 below 0.2 ppm). The methodology consists of: (a) a multiple internal lock correction (here ten masses; no identity of internal lock masses is required) to avoid suppression problems of a single internal lock mass as well as to increase lock precision, (b) a multiple external mass correction (here 211 masses) to correct for calibration errors, (c) intensity dependant mass correction, (d) file averaging. The strategy is supported by ultra-fast file searching of baseline corrected, noise-reduced metAlign output. The output and efficiency of ultra-fast searching is essential in obtaining the required information to visualize the distribution of mass errors and isotope ratio deviations as a function of mass and intensity.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-010-0230-y) contains supplementary material, which is available to authorized users.

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