Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models

Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High throughput annotation and metabolic modeling of these genomes is now a reality. The high throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represents the next frontier in microbial bioinformatics. The fruition of this next frontier will depend upon the integration of numerous data sources relating to mechanisms, components, and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data, and binding site locations, and we explore how these data are being used for the reconstruction of new regulatory networks. From template network based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. Finally, we explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.J.P.F. acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. The work was supported in part by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), National Funds through the FCT within projects [FCOMP-01-0124-FEDER015079] (ToMEGIM—Computational Tools for Metabolic Engineering using Genome-scale Integrated Models) and FCOMP-01-0124-FEDER009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori), the U.S. Department of Energy under contract [DE-ACO2-06CH11357] and the National Science Foundation under [0850546]

Jupiter’s Synchrotron Radiation Throughout the Comet P/Shoemaker-Levy 9 Impact Period

Jupiter’s microwave emission was observed throughout the SL9 impact period by many different telescopes, among which the NRAO 140-foot telescope in Green Bank (21 cm), Westerbork (92 cm), Effelsberg (6, 11 cm), Parkes (21 cm), NASA DSN (13 cm), and the Very Large Array (22, 90 cm). We determined the “average” total nonthermal flux density from the planet after having subtracted the thermal contribution, following the formulation by de Pater and Klein, (1989) and Klein et al., (1989). The flux density increased typically by 40-50% at 6 cm wavelength, 27% at 11-13 cm, 22%at 21 cm and 10-15% at 90 cm. Thus the radio spectrum hardened considerably during the week of cometary impacts. Following the week of cometary impacts, the flux density began to subside at all wavelength.VLA images show the brightness distribution of the planet; a comparison of images taken before and during the week of impacts show marked changes in the brightness distribution. At a central meridian longitude λIII≈ 110°, the left side of the belts increased considerably and moved inwards by ~ 0.2 RJ. This suggests that the increase in flux density is caused by energization of the resident particle population.</jats:p

A genome-wide meta-analysis of nodular sclerosing Hodgkin lymphoma identifies risk loci at 6p21.32

Nodular sclerosing Hodgkin lymphoma (NSHL) is a distinct, highly heritable Hodgkin lymphoma subtype. We undertook a genome-wide meta-analysis of 393 European-origin adolescent/young adult NSHL patients and 3315 controls using the Illumina Human610-Quad Beadchip and Affymetrix Genome-Wide Human SNP Array 6.0. We identified 3 single nucleotide polymorphisms (SNPs) on chromosome 6p21.32 that were significantly associated with NSHL risk: rs9268542 (P = 5.35 × 10(−10)), rs204999 (P = 1.44 × 10(−9)), and rs2858870 (P = 1.69 × 10(−8)). We also confirmed a previously reported association in the same region, rs6903608 (P = 3.52 × 10(−10)). rs204999 and rs2858870 were weakly correlated (r(2) = 0.257), and the remaining pairs of SNPs were not correlated (r(2) < 0.1). In an independent set of 113 NSHL cases and 214 controls, 2 SNPs were significantly associated with NSHL and a third showed a comparable odds ratio (OR). These SNPs are found on 2 haplotypes associated with NSHL risk (rs204999-rs9268528-rs9268542-rs6903608-rs2858870; AGGCT, OR = 1.7, P = 1.71 × 10(−6); GAATC, OR = 0.4, P = 1.16 × 10(−4)). All individuals with the GAATC haplotype also carried the HLA class II DRB1*0701 allele. In a separate analysis, the DRB1*0701 allele was associated with a decreased risk of NSHL (OR = 0.5, 95% confidence interval = 0.4, 0.7). These data support the importance of the HLA class II region in NSHL etiology