Chemical Priming by Isothiocyanates Protects Against Intoxication by Products of the Mustard Oil Bomb.

In Brassicaceae, tissue damage triggers the mustard oil bomb i.e., activates the degradation of glucosinolates by myrosinases leading to a rapid accumulation of isothiocyanates at the site of damage. Isothiocyanates are reactive electrophilic species (RES) known to covalently bind to thiols in proteins and glutathione, a process that is not only toxic to herbivores and microbes but can also cause cell death of healthy plant tissues. Previously, it has been shown that subtoxic isothiocyanate concentrations can induce transcriptional reprogramming in intact plant cells. Glutathione depletion by RES leading to breakdown of the redox potential has been proposed as a central and common RES signal transduction mechanism. Using transcriptome analyses, we show that after exposure of Arabidopsis seedlings (grown in liquid culture) to subtoxic concentrations of sulforaphane hundreds of genes were regulated without depletion of the cellular glutathione pool. Heat shock genes were among the most highly up-regulated genes and this response was found to be dependent on the canonical heat shock factors A1 (HSFA1). HSFA1-deficient plants were more sensitive to isothiocyanates than wild type plants. Moreover, pretreatment of Arabidopsis seedlings with subtoxic concentrations of isothiocyanates increased resistance against exposure to toxic levels of isothiocyanates and, hence, may reduce the autotoxicity of the mustard oil bomb by inducing cell protection mechanisms.

Neisseria meningitidis causes cell cycle arrest of human brain microvascular endothelial cells at S phase via p21 and cyclin G2.

Microbial pathogens have developed several mechanisms to modulate and interfere with host cell cycle progression. In this study, we analysed the effect of the human pathogen Neisseria meningitidis on cell cycle in a brain endothelial cell line as well as in primary brain endothelial cells. We found that N. Meningitidis causes an accumulation of cells in the S phase early at 3 and at 24 h post-infection that was paralleled by a decrease of cells in G2/M phase. Importantly, the outer membrane proteins of the colony opacity-associated (Opa) protein family as well as the Opc protein proved to trigger the accumulation of cells in the S phase. A focused cell cycle reverse transcription quantitative polymerase chain reaction-based array and integrated network analysis revealed changes in the abundance of several cell cycle regulatory mRNAs, including the cell cycle inhibitors p21(WAF1/CIP1) and cyclin G2. These alterations were reflected in changes in protein expression levels and/or relocalization in N. meningitidis-infected cells. Moreover, an increase in p21(WAF1/CIP1) expression was found to be p53 independent. Genetic ablation of p21(WAF1/CIP1) and cyclin G2 abrogated N. meningitidis-induced S phase accumulation. Finally, by measuring the levels of the biomarker 8-hydroxydeoxyguanosine and phosphorylation of the histone variant H2AX, we provide evidence that N. meningitidis induces oxidative DNA damage in infected cells.

Integrated inference and evaluation of host-fungi interaction networks.

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host-pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host-fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen-host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi-human and fungi-mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host-fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host-fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host-fungi transcriptome and proteome data.

Transcriptional landscape and essential genes of Neisseria gonorrhoeae.

The WHO has recently classified Neisseria gonorrhoeae as a super-bacterium due to the rapid spread of antibiotic resistant derivatives and an overall dramatic increase in infection incidences. Genome sequencing has identified potential genes, however, little is known about the transcriptional organization and the presence of non-coding RNAs in gonococci. We performed RNA sequencing to define the transcriptome and the transcriptional start sites of all gonococcal genes and operons. Numerous new transcripts including 253 potentially non-coding RNAs transcribed from intergenic regions or antisense to coding genes were identified. Strikingly, strong antisense transcription was detected for the phase-variable opa genes coding for a family of adhesins and invasins in pathogenic Neisseria, that may have regulatory functions. Based on the defined transcriptional start sites, promoter motifs were identified. We further generated and sequenced a high density Tn5 transposon library to predict a core of 827 gonococcal essential genes, 133 of which have no known function. Our combined RNA-Seq and Tn-Seq approach establishes a detailed map of gonococcal genes and defines the first core set of essential gonococcal genes.

Robustness and accuracy of functional modules in integrated network analysis.

MOTIVATION: High-throughput molecular data provide a wealth of information that can be integrated into network analysis. Several approaches exist that identify functional modules in the context of integrated biological networks. The objective of this study is 2-fold: first, to assess the accuracy and variability of identified modules and second, to develop an algorithm for deriving highly robust and accurate solutions. RESULTS: In a comparative simulation study accuracy and robustness of the proposed and established methodologies are validated, considering various sources of variation in the data. To assess this variation, we propose a jackknife resampling procedure resulting in an ensemble of optimal modules. A consensus approach summarizes the ensemble into one final module containing maximally robust nodes and edges. The resulting consensus module identifies and visualizes robust and variable regions by assigning support values to nodes and edges. Finally, the proposed approach is exemplified on two large gene expression studies: diffuse large B-cell lymphoma and acute lymphoblastic leukemia.

The transcriptional landscape of Chlamydia pneumoniae.

BACKGROUND: Gene function analysis of the obligate intracellular bacterium Chlamydia pneumoniae is hampered by the facts that this organism is inaccessible to genetic manipulations and not cultivable outside the host. The genomes of several strains have been sequenced; however, very little information is available on the gene structure and transcriptome of C. pneumoniae. RESULTS: Using a differential RNA-sequencing approach with specific enrichment of primary transcripts, we defined the transcriptome of purified elementary bodies and reticulate bodies of C. pneumoniae strain CWL-029; 565 transcriptional start sites of annotated genes and novel transcripts were mapped. Analysis of adjacent genes for co-transcription revealed 246 polycistronic transcripts. In total, a distinct transcription start site or an affiliation to an operon could be assigned to 862 out of 1,074 annotated protein coding genes. Semi-quantitative analysis of mapped cDNA reads revealed significant differences for 288 genes in the RNA levels of genes isolated from elementary bodies and reticulate bodies. We have identified and in part confirmed 75 novel putative non-coding RNAs. The detailed map of transcription start sites at single nucleotide resolution allowed for the first time a comprehensive and saturating analysis of promoter consensus sequences in Chlamydia. CONCLUSIONS: The precise transcriptional landscape as a complement to the genome sequence will provide new insights into the organization, control and function of genes. Novel non-coding RNAs and identified common promoter motifs will help to understand gene regulation of this important human pathogen.

BioNet: an R-Package for the functional analysis of biological networks.

MOTIVATION: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. RESULTS: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. AVAILABILITY: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.

Identifying functional modules in protein-protein interaction networks: an integrated exact approach.

MOTIVATION: With the exponential growth of expression and protein-protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature. RESULTS: Here we present the first exact solution for this problem, which is based on integer-linear programming and its connection to the well-known prize-collecting Steiner tree problem from Operations Research. Despite the NP-hardness of the underlying combinatorial problem, our method typically computes provably optimal subnetworks in large PPI networks in a few minutes. An essential ingredient of our approach is a scoring function defined on network nodes. We propose a new additive score with two desirable properties: (i) it is scalable by a statistically interpretable parameter and (ii) it allows a smooth integration of data from various sources. We apply our method to a well-established lymphoma microarray dataset in combination with associated survival data and the large interaction network of HPRD to identify functional modules by computing optimal-scoring subnetworks. In particular, we find a functional interaction module associated with proliferation over-expressed in the aggressive ABC subtype as well as modules derived from non-malignant by-stander cells. AVAILABILITY: Our software is available freely for non-commercial purposes at http://www.planet-lisa.net.

Increased in vivo frequency of IA-2 peptide-reactive IFNgamma+/IL-4- T cells in type 1 diabetic subjects.

Active T cell recognition of islet antigens has been postulated as the pathogenic mechanism in human type 1 diabetes, but evidence is scarce. If T cells are engaged, they are expected to display increased clonal size and exhibit a T helper (Th)1/Th2 differentiation state. We used a peptide library that covers tyrosine phosphatase IA-2, a target antigen expressed in pancreatic beta cells, to probe 8 diabetic patients and 5 HLA-matched controls. When tested in a high resolution IFNgamma/IL-4 double color ELISPOT assay directly ex vivo, the number of IA-2-reactive IFNgamma producing cells was 17-fold higher in patients than in controls and IL-4 producing cells were not present. An average of 9 peptides was recognized in the patients vs. one in the controls. Determinant recognition primarily involved CD4+ cells and showed high variability among the patients. Furthermore, anti-CD28 antibody signal enhances quantitative assessment of effector T cells in T1D patients. In vitro expansion with peptides and IL-2 results in detection of responding cells in the controls and loss of disease specificity of the T cell response. Together these data provide strong evidence for the active targeting of IA-2 by Th1 memory effector cells in human type 1 diabetes.

T cells recognize multiple GAD65 and proinsulin epitopes in human type 1 diabetes, suggesting determinant spreading.

Human type 1 diabetes is thought to be mediated by autoreactive T cells specific for antigens expressed by pancreatic beta cells. However, it is unclear which autoantigens and determinants thereof are the targets of the autoimmune attack. Using comprehensive peptide libraries that cover the entire sequence of two major candidate autoantigens, GAD65 and proinsulin, we measured the in vivo frequencies of peptide-specific, IFN-gamma-producing memory T cells in 27 diabetic patients, 14 high risk individuals, and 15 partially HLA-matched healthy controls. Compared to the controls, both a higher number of determinants on the islet cell antigens were recognized and the frequencies of peptide specific cells were increased in patients and high risk individuals. Inclusion of signal enhancing anti-CD28 antibody further accentuated this difference. Considerable heterogeneity in peptide recognition was seen even in DRB1*04, DQB1*0302 matched individuals. Unlike its peptides, the GAD protein antigen did not recall a T cell memory response. The highly heterogeneous recognition of a multitude of peptide determinants on both autoantigens, occurring in the absence of protein recognition, and the low functional avidity of the memory cells involved jointly suggest that the autoimmune T cell repertoire in human type 1 diabetes primarily targets cryptic determinants engaged by determinant spreading.