SUMMARY

Genomic rearrangements are thought to occur progressively during tumor development. Recent findings, however, suggest an alternative mechanism, involving massive chromosome rearrangements in a one-step catastrophic event termed chromothripsis. We report the whole-genome sequencing-based analysis of a Sonic-Hedgehog medulloblastoma (SHH-MB) brain tumor from a patient with a germline TP53 mutation (Li-Fraumeni syndrome), uncovering massive, complex chromosome rearrangements. Integrating TP53 status with microarray and deep sequencing-based DNA rearrangement data in additional patients reveals a striking association between TP53 mutation and chromothripsis in SHH-MBs. Analysis of additional tumor entities substantiates a link between TP53 mutation and chromothripsis, and indicates a context-specific role for p53 in catastrophic DNA rearrangements. Among these, we observed a strong association between somatic TP53 mutations and chromothripsis in acute myeloid leukemia. These findings connect p53 status and chromothripsis in specific tumor types, providing a genetic basis for understanding particularly aggressive subtypes of cancer.

miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression and apoptosis. Here, we show that miRNAs encoded by this cluster and sharing the seed sequence of miR-17 exert their influence on one of the most essential cellular processes – endocytic trafficking. By mRNA expression analysis we identified that regulation of endocytic trafficking by miR-17 can potentially be achieved by targeting of a number of trafficking regulators. We have thoroughly validated TBC1D2/Armus, a GAP of Rab7 GTPase, as a novel target of miR-17. Our study reveals regulation of endocytic trafficking as a novel function of miR-17, which might act cooperatively with other functions of miR-17 and related miRNAs in health and disease.

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. We present an elaborate analysis pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells. We interrogated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed time-lapse screening of gene knockdowns in neuroblastoma cells. We classified cellular phenotypes and used the temporal context of the perturbation effect to determine the sequence of events, particularly the mitotic entry preceding cell death. Based upon this phenotype kinetics from the gene knockdown screening, we inferred dynamic gene functions in mitosis and cell proliferation. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) with a vital role in mitosis and these are promising therapeutic targets for neuroblastoma. Images and movies of every time point of all screened genes are available at https://ichip.bioquant.uni-heidelberg.de.

SUMMARY

Hepatitis C virus (HCV) is a major causative agent of chronic liver disease in humans. To gain insight into host factor requirements for HCV replication we performed a siRNA screen of the human kinome and identified 13 different kinases, including phosphatidylinositol-4 kinase III alpha (PI4KIIIα) as required for HCV replication. Consistent with elevated levels of the PI4KIIIα product phosphatidylinositol-4-phosphate (PI4P) detected in HCV infected cultured hepatocytes and liver tissue from chronic hepatitis C patients, the enzymatic activity of PI4KIIIα was critical for HCV replication. Viral nonstructural protein 5A (NS5A) was found to interact with PI4KIIIα and stimulate its kinase activity. The absence of PI4KIIIα activity induced a dramatic change in the ultrastructural morphology of the membranous HCV replication complex. Our analysis suggests that the direct activation of a lipid kinase by HCV NS5A contributes critically to the integrity of the membranous viral replication complex.

The observed motion of subcellular particles in fluorescence microscopy image sequences of live cells is generally a superposition of the motion and deformation of the cell and the motion of the particles. Decoupling the two types of movements to enable accurate classification of the particle motion requires the application of registration algorithms. We have developed an intensity-based approach for nonrigid registration of multi-channel microscopy image sequences of cell nuclei. First, based on 3-D synthetic images we demonstrate that cell nucleus deformations change the observed motion types of particles and that our approach allows to recover the original motion. Second, we have successfully applied our approach to register 2-D and 3-D real microscopy image sequences. A quantitative experimental comparison with previous approaches for nonrigid registration of cell microscopy has also been performed.

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis.

Background

High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology.

Results

We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach.

Conclusions

Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.

Objectives

Autoimmune pancreatitis (AIP) is thought to be an immune-mediated inflammatory process, directed against the epithelial components of the pancreas.

Methods

In order to explore key targets of the inflammatory process we analysed the expression of proteins at the RNA and protein level using genomics and proteomics, immunohistochemistry, Western blot and immunoassay. An animal model of AIP with LP-BM5 murine leukemia virus infected mice was studied in parallel. RNA microarrays of pancreatic tissue from 12 patients with AIP were compared to those of 8 patients with non-AIP chronic pancreatitis (CP).

Results

Expression profiling revealed 272 upregulated genes, including those encoding for immunoglobulins, chemokines and their receptors, and 86 downregulated genes, including those for pancreatic proteases such as three trypsinogen isoforms. Protein profiling showed that the expression of trypsinogens and other pancreatic enzymes was greatly reduced. Immunohistochemistry demonstrated a near-loss of trypsin positive acinar cells, which was also confirmed by Western blotting. The serum of AIP patients contained high titres of autoantibodies against the trypsinogens PRSS1, and PRSS2 but not against PRSS3. In addition, there were autoantibodies against the trypsin inhibitor PSTI (the product of the SPINK1 gene). In the pancreas of AIP animals we found similar protein patterns and a reduction in trypsinogen.

Conclusion

These data indicate that the immune-mediated process characterizing AIP involves pancreatic acinar cells and their secretory enzymes such as trypsin isoforms. Demonstration of trypsinogen autoantibodies may be helpful for the diagnosis of AIP.

During development and differentiation of an organism, accurate gene regulation is central for cells to maintain and balance their differentiation processes. Transcriptional interactions between cis-acting DNA elements such as promoters and enhancers are the basis for precise and balanced transcriptional regulation. We identified modules of combinations of binding sites in proximal and distal regulatory regions upstream of all transcription start sites (TSSs) in silico and applied these modules to gene expression time-series of mouse embryonic development and differentiation of human stem cells. In addition to tissue-specific regulation controlled by combinations of transcription factors (TFs) binding at promoters, we observed that in particular the combination of TFs binding at promoters together with TFs binding at the respective enhancers regulate highly specifically temporal progression during development: whereas 40% of TFs were specific for time intervals, 79% of TF pairs and even 97% of promoter–enhancer modules showed specificity for single time intervals of the human stem cells. Predominantly SP1 and E2F contributed to temporal specificity at promoters and the forkhead (FOX) family of TFs at enhancer regions. Altogether, we characterized three classes of TFs: with binding sites being enriched at the TSS (like SP1), depleted at the TSS (like FOX), and rather uniformly distributed.

A workflow is presented that integrates gene expression data, proteomic data, and literature-based manual curation to construct multicellular, tissue-specific models of human brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types. Three analyses are applied for gene identification, analysis of omics data, and analysis of physiological states. First, we identify glutamate decarboxylase as a target that may contribute to cell-type and regional specificity in Alzheimer’s disease. Second, the decreased metabolic rate seen in affected brain regions in Alzheimer’s disease is consistent with a suppression of central metabolic gene expression in histopathologically normal neurons. Third, we identify pathways in cholinergic neurons that couple mitochondrial metabolism and cytosolic acetylcholine production, and subsequently find that cholinergic neurotransmission accounts for ∼3% of brain neurotransmission. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in human tissues, and provide detailed mechanistic insight into high-throughput data analysis.

Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the ~21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

Background

Autophagy mediates lysosomal degradation of cytosolic components. Recent work has associated autophagic dysfunction with pathologies, including cancer and cardiovascular disease. To date, the identification of clinically-applicable drugs that modulate autophagy has been hampered by the lack of standardized assays capable of precisely reporting autophagic activity.

Results

We developed and implemented a high-content, flow-cytometry-based screening approach for rapid, precise, and quantitative measurements of pharmaceutical control over autophagy. Our assay allowed for time-resolved individual measurements of autolysosome formation and degradation, and endolysosomal activities under both basal and activated autophagy conditions. As proof of concept, we analyzed conventional autophagy regulators, including cardioprotective compounds aminoimidazole carboxamide ribonucleotide (AICAR), rapamycin, and resveratrol, and revealed striking conditional dependencies of rapamycin and autophagy inhibitor 3-methyladenine (3-MA). To identify novel autophagy modulators with translational potential, we screened the Prestwick Chemical Library of 1,120 US Food and Drug Administration (FDA)-approved compounds for impact on autolysosome formation. In all, 38 compounds were identified as potential activators, and 36 as potential inhibitors of autophagy. Notably, amongst the autophagy enhancers were cardiac glycosides, from which we selected digoxin, strophanthidin, and digoxigenin for validation by standard biochemical and imaging techniques. We report the induction of autophagic flux by these cardiac glycosides, and the concentrations allowing for specific enhancement of autophagic activities without impact on endolysosomal activities.

Conclusions

Our systematic analysis of autophagic and endolysosomal activities outperformed conventional autophagy assays and highlights the complexity of drug influence on autophagy. We demonstrate conditional dependencies of established regulators. Moreover, we identified new autophagy regulators and characterized cardiac glycosides as novel potent inducers of autophagic flux.

Background

The β-amyloid precursor protein (APP) and the related β-amyloid precursor-like proteins (APLPs) undergo complex proteolytic processing giving rise to several fragments. Whereas it is well established that Aβ accumulation is a central trigger for Alzheimer's disease, the physiological role of APP family members and their diverse proteolytic products is still largely unknown. The secreted APPsα ectodomain has been shown to be involved in neuroprotection and synaptic plasticity. The γ-secretase-generated APP intracellular domain (AICD) functions as a transcriptional regulator in heterologous reporter assays although its role for endogenous gene regulation has remained controversial.

Results

To gain further insight into the molecular changes associated with knockout phenotypes and to elucidate the physiological functions of APP family members including their proposed role as transcriptional regulators, we performed DNA microarray transcriptome profiling of prefrontal cortex of adult wild-type (WT), APP knockout (APP-/-), APLP2 knockout (APLP2-/-) and APPsα knockin mice (APPα/α) expressing solely the secreted APPsα ectodomain. Biological pathways affected by the lack of APP family members included neurogenesis, transcription, and kinase activity. Comparative analysis of transcriptome changes between mutant and wild-type mice, followed by qPCR validation, identified co-regulated gene sets. Interestingly, these included heat shock proteins and plasticity-related genes that were both down-regulated in knockout cortices. In contrast, we failed to detect significant differences in expression of previously proposed AICD target genes including Bace1, Kai1, Gsk3b, p53, Tip60, and Vglut2. Only Egfr was slightly up-regulated in APLP2-/- mice. Comparison of APP-/- and APPα/α with wild-type mice revealed a high proportion of co-regulated genes indicating an important role of the C-terminus for cellular signaling. Finally, comparison of APLP2-/- on different genetic backgrounds revealed that background-related transcriptome changes may dominate over changes due to the knockout of a single gene.

Conclusion

Shared transcriptome profiles corroborated closely related physiological functions of APP family members in the adult central nervous system. As expression of proposed AICD target genes was not altered in adult cortex, this may indicate that these genes are not affected by lack of APP under resting conditions or only in a small subset of cells.

A systems biology–based approach shows that life and death decisions for cells depend on the stoichiometry of c-FLIP isoforms.

Cellular FADD-like interleukin-1β–converting enzyme inhibitory proteins (c-FLIPs; isoforms c-FLIP long [c-FLIPL], c-FLIP short [c-FLIPS], and c-FLIP Raji [c-FLIPR]) regulate caspase-8 activation and death receptor (DR)–induced apoptosis. In this study, using a combination of mathematical modeling, imaging, and quantitative Western blots, we present a new mathematical model describing caspase-8 activation in quantitative terms, which highlights the influence of c-FLIP proteins on this process directly at the CD95 death-inducing signaling complex. We quantitatively define how the stoichiometry of c-FLIP proteins determines sensitivity toward CD95-induced apoptosis. We show that c-FLIPL has a proapoptotic role only upon moderate expression in combination with strong receptor stimulation or in the presence of high amounts of one of the short c-FLIP isoforms, c-FLIPS or c-FLIPR. Our findings resolve the present controversial discussion on the function of c-FLIPL as a pro- or antiapoptotic protein in DR-mediated apoptosis and are important for understanding the regulation of CD95-induced apoptosis, where subtle differences in c-FLIP concentrations determine life or death of the cells.

A phenocopy is defined as an environmentally induced phenotype of one individual which is identical to the genotype-determined phenotype of another individual. The phenocopy phenomenon has been translated to the drug discovery process as phenotypes produced by the treatment of biological systems with new chemical entities (NCE) may resemble environmentally induced phenotypic modifications. Various new chemical entities exerting inhibition of the kinase activity of Transforming Growth Factor β Receptor I (TGF-βR1) were qualified by high-throughput RNA expression profiling. This chemical genomics approach resulted in a precise time-dependent insight to the TGF-β biology and allowed furthermore a comprehensive analysis of each NCE's off-target effects. The evaluation of off-target effects by the phenocopy approach allows a more accurate and integrated view on optimized compounds, supplementing classical biological evaluation parameters such as potency and selectivity. It has therefore the potential to become a novel method for ranking compounds during various drug discovery phases.

Background

In the past, molecular mechanisms that drive the initiation of an inflammatory response have been studied intensively. However, corresponding mechanisms that sustain the expression of inflammatory response genes and hence contribute to the establishment of chronic disorders remain poorly understood. Recently, we provided genetic evidence that signaling via the receptor for advanced glycation end products (Rage) drives the strength and maintenance of an inflammatory reaction. In order to decipher the mode of Rage function on gene transcription levels during inflammation, we applied global gene expression profiling on time-resolved samples of mouse back skin, which had been treated with the phorbol ester TPA, a potent inducer of skin inflammation.

Results

Ranking of TPA-regulated genes according to their time average mean and peak expression and superimposition of data sets from wild-type (wt) and Rage-deficient mice revealed that Rage signaling is not essential for initial changes in TPA-induced transcription, but absolutely required for sustained alterations in transcript levels. Next, we used a data set of differentially expressed genes between TPA-treated wt and Rage-deficient skin and performed computational analysis of their proximal promoter regions. We found a highly significant enrichment for several transcription factor binding sites (TFBS) leading to the prediction that corresponding transcription factors, such as Sp1, Tcfap2, E2f, Myc and Egr, are regulated by Rage signaling. Accordingly, we could confirm aberrant expression and regulation of members of the E2f protein family in epidermal keratinocytes of Rage-deficient mice.

Conclusions

In summary, our data support the model that engagement of Rage converts a transient cellular stimulation into sustained cellular dysfunction and highlight a novel role of the Rb-E2f pathway in Rage-dependent inflammation during pathological conditions.

Background

Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.

Methods

For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.

Results

Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.

Conclusion

We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.

Motivation: Detecting human proteins that are involved in virus entry and replication is facilitated by modern high-throughput RNAi screening technology. However, hit lists from different laboratories have shown only little consistency. This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis. We wanted to improve reliability of such screens by combining a population analysis of infected cells with an established dye intensity readout.

Results: Viral infection is mainly spread by cell–cell contacts and clustering of infected cells can be observed during spreading of the infection in situ and in vivo. We employed this clustering feature to define knockdowns which harm viral infection efficiency of human Hepatitis C Virus. Images of knocked down cells for 719 human kinase genes were analyzed with an established point pattern analysis method (Ripley's K-function) to detect knockdowns in which virally infected cells did not show any clustering and therefore were hindered to spread their infection to their neighboring cells. The results were compared with a statistical analysis using a common intensity readout of the GFP-expressing viruses and a luciferase-based secondary screen yielding five promising host factors which may suit as potential targets for drug therapy.

Conclusion: We report of an alternative method for high-throughput imaging methods to detect host factors being relevant for the infection efficiency of viruses. The method is generic and has the potential to be used for a large variety of different viruses and treatments being screened by imaging techniques.

Contact: r.eils@dkfz.de; r.koenig@dkfz.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity.

When prokaryotes are maintained at early- to mid-log phase growth through serial passaging for hundreds of generations, the strains improve fitness and evolve a higher growth rate (Lenski and Travisano, 1994; Ibarra et al, 2002). This increased growth rate is the result of the appearance of a few causal mutations (Herring et al, 2006; Conrad et al, 2009). In Escherichia coli, these altered growth phenotypes are consistent with predictions from genome-scale models of metabolism (GEMs) (Ibarra et al, 2002; Fong and Palsson, 2004). However, it is still not known (1) whether absolute gene and protein expression levels and expression changes are consistent with optimal growth predictions from in silico GEMs or (2) whether measured expression changes can be linked to physiological changes that are based on known mechanisms or pathways. In this study, we begin to address these questions using constraint-based modeling of E. coli K-12 metabolism (Feist and Palsson, 2008) to analyze omic data that document the expression changes in E. coli under adaptive evolution in three different growth conditions.

Mapping high-throughput data to a network can be useful for interpretation. However, it does not account for upstream and downstream effects of gene and protein expression changes. The analysis of data in the context of GEMs can suggest if predicted activity is consistent with the data. For this work, we used a variant of flux balance analysis (FBA), called Parsimonious enzyme usage FBA (pFBA) (Figure 1), to classify all genes according to whether they are used in the optimal growth solutions. Results from these models were compared with the data to assess whether the data were consistent with genes and proteins within the predicted optimal solutions, and whether the expression changes were consistent with measured physiology. Through this analysis, we find that the data provide a high coverage of genes that contribute to the optimal growth solutions (Figure 1B). In fact, the union of the proteomic and transcriptomic data for non-essential genes provides support for 97.7% of all non-essential gene-associated reactions within the optimal growth predictions. Thus, the spectrum of expressed genes and proteins is consistent with the pathway utilization that is predicted for these optimal growth phenotypes.

Laboratory-evolved strains attain a higher growth rate. This higher growth rate is usually associated with an increased substrate uptake rate (Ibarra et al, 2002; Fong et al, 2005) and in some cases more efficient metabolism (Ibarra et al, 2002). Both of these properties are also witnessed in the strains studied here. It has been reported that in most cases, evolved strain growth phenotype is consistent with GEM predictions (Ibarra et al, 2002; Teusink et al, 2009). Here, we evaluate whether the laboratory-evolved strains adjust the gene and protein expression levels in accordance with pathway usage in the optimal growth predictions. Essential and non-essential genes and proteins within the optimal growth solutions are significantly upregulated (Figure 1B). This suggests that these proteins may be acting as bottlenecks that are relieved through the adaptive process, thereby allowing for a higher substrate uptake rate and growth rate. However, genes and proteins associated with reactions that cannot carry a flux in the given growth conditions are downregulated in the evolved strains (Figure 1B). Furthermore, there is downregulation of genes associated with less efficient pathways (Figure 5C). Thus, the omic data support the emergence of the predicted optimal growth states, consistent with the increased substrate uptake upstream and the increased biomass production downstream of these internal pathways.

Regulatory mechanisms, both known and unknown, are responsible for the changes seen here. Across all data sets, several metabolic regulons are significantly downregulated. However, no known regulons were enriched among upregulated genes or proteins for all but one data set. Aside from just regulating the metabolic pathways directly, these mechanisms lead to additional physiological changes. For example, in the minimal media growth conditions used here, the stringent response normally represses growth while upregulating amino-acid biosynthetic processes. However, evolved strain gene expression shows a suppression of the stringent response, as evolved strain gene expression shows either no expression change or changes opposite to the normal stringent response.

The implications of this work are as follows: (1) genome-scale gene and protein expression data are consistent with FBA computed optimal growth states, and evolved strains reinforce these optimal states; (2) genome-scale models will have an important function bridging the gap between genotype and phenotype; and (3) the development of additional genome-scale models of other growth-related processes such as transcription and translation (Thiele et al, 2009) will have an important function in elucidating the mechanisms that contribute the most to altered phenotypes (Lewis et al, 2009a). In addition, reconstruction of the transcriptional regulation network will aid in identifying the control of expression changes seen in the other systems.

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.

Background

Normalization of microarrays is a standard practice to account for and minimize effects which are not due to the controlled factors in an experiment. There is an overwhelming number of different methods that can be applied, none of which is ideally suited for all experimental designs. Thus, it is important to identify a normalization method appropriate for the experimental setup under consideration that is neither too negligent nor too stringent. Major aim is to derive optimal results from the underlying experiment. Comparisons of different normalization methods have already been conducted, none of which, to our knowledge, comparing more than a handful of methods.

Results

In the present study, 25 different ways of pre-processing Illumina Sentrix BeadChip array data are compared. Among others, methods provided by the BeadStudio software are taken into account. Looking at different statistical measures, we point out the ideal versus the actual observations. Additionally, we compare qRT-PCR measurements of transcripts from different ranges of expression intensities to the respective normalized values of the microarray data. Taking together all different kinds of measures, the ideal method for our dataset is identified.

Conclusions

Pre-processing of microarray gene expression experiments has been shown to influence further downstream analysis to a great extent and thus has to be carefully chosen based on the design of the experiment. This study provides a recommendation for deciding which normalization method is best suited for a particular experimental setup.

Background

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.

Results

We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.

Conclusions

Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.

CD95-mediated apoptotic and NF-κB signaling were described by a simple kinetic model. We used a model reduction technique to reduce the number of reactions from 92 to 23 while maintaining a good model fit.p43-FLIP, which is generated at the CD95 DISC by procaspase-8 cleavage, was found to be the link between the CD95 DISC and the NF-κB pathway. P43-FLIP interacts with the IKK complex and leads to its activation.The CD95 DISC complex acts as a signal processor that diverges signals into the apoptotic and NF-κB pathways depending on the amounts of specific DISC proteins.Life/death decisions in CD95 signaling are determined by c-FLIPL and procaspase-8 in a non-linear way.

The CD95 protein (APO-1/Fas; Krammer et al, 2007) is a member of the death receptor family. Signal transduction of CD95 starts with the formation of the death-inducing signaling complex (DISC) detectable within seconds after receptor stimulation (Kischkel et al, 1995). The DISC consists of CD95, the adaptor molecule FADD, procaspase-8/10 and c-FLIPL/S/R (Muzio et al, 1996; Scaffidi et al, 1999; Sprick et al, 2002; Golks et al, 2005; Krammer et al, 2007). Procaspase-8 is converted at the DISC, in a series of autoproteolytic cleavage steps, to p43/p41 and p18, which leads to the activation of effector caspase-3 and demolition of the cell. Recently, experiments have demonstrated that CD95L also activates the induction of transcription factor NF-κB (Barnhart et al, 2004; Kreuz et al, 2004; Peter et al, 2007). It was shown that DED-containing proteins at the DISC, such as procaspase-8 and c-FLIP have a complex role in NF-κB activation (Chaudhary et al, 2000; Hu et al, 2000; Kreuz et al, 2004; Dohrman et al, 2005; Su et al, 2005). These findings motivated our systems biology approach and prompted us to determine whether CD95-mediated signaling should be considered a dynamic system, resulting in life/death decisions.

We observed simultaneous apoptosis and NF-κB induction on CD95 stimulation in HeLa cells stably overexpressing CD95–GFP (HeLa-CD95) using biochemical approaches and live-cell imaging. To understand the crosstalk between CD95-mediated apoptosis and NF-κB activation, we created a mathematical model of CD95 signaling. Our model assumes a trimerized ligand (L) that binds to a trimerized CD95 receptor (R) that can recruit three copies of FADD (F) leading to the DISC formation. Subsequently, DED-containing proteins, such as procaspase-8 (C8), c-FLIPL (FL) and c-FLIPS (FS) can bind to FADD. The order of protein binding gives rise to a combinatorial variety of intermediates, resulting either in the formation of the cleavage product of procaspase-8: p43/p41, or in the formation of the cleavage product of c-FLIPL: p43-FLIP. p43/p41 gives rise to signaling in the apoptotic branch of the model, whereas the cleavage product p43-FLIP triggers the activation of NF-κB. The model postulates that p43-FLIP interacts with the IKK complex leading to the phosphorylation of IκB (NF-κB·IκB·P), which entails its degradation and the translocation of p65 to the nucleus (NF-κB*). As a validation of the model topology, we confirmed experimentally that p43-FLIP interacts with the IKK complex and subsequently leads to its activation.

The complete model could be fitted well to a data set derived from quantitative western blots of a number of key proteins of the apoptotic and NF-κB pathways. However, we tested whether all the 92 reactions were required to reproduce the observed dynamics, as a small model would yield more reliable parameter estimates, which in turn would increase its usefulness as a predictive tool. To determine the most important interactions, we simplified the complete model in a step-wise manner obtaining a model of considerably lower complexity (Figure 5A, simplification steps are listed in Figure 5B). The final reduced model still approximated well the experimental data set (Figure 5D), whereas the number of reactions decreased from 92 to 23 (Figure 5C).

To better understand the interplay of DISC proteins in the determination of cell fate, we analyzed the activity of caspase-3 and NF-κB as a function of procaspase-8 and c-FLIPL levels (Figure 8A). We observed in our simulations that the decision over apoptosis and NF-κB is controlled by both proteins. Different scenarios occur that show combination or absence of either caspase-3 or NF-κB activity. The phase diagram shown in Figure 8A predicts that either increasing or decreasing the amount of c-FLIPL leads to a different signaling mode. We sought to validate this prediction by downregulating or overexpressing procaspase-8 and c-FLIPL, respectively, in HeLa-CD95 cells and measuring CD95-mediated signaling. In agreement with the phase diagram (Figure 8A), we observed that c-FLIPL overexpression resulted in a strong reduction of apoptosis (Figure 8D). Furthermore, we could further confirm by western blot analysis that the stable knockdown of c-FLIPL and procaspase-8 led to a reduction of the levels of p43-FLIP and phosphorylated IκBα after receptor stimulation (Figure 8C and D). In addition, to control the specificity of c-FLIP downregulation and further confirm the requirement of cleavage of c-FLIPL to p43-FLIP, we performed a reconstitution experiment in HeLa-CD95–c-FLIP-deficient cells (Figure 8E). Cells reconstituted with WT c-FLIPL were able to generate p43-FLIP and increased IκBα phosphorylation on CD95 stimulation. In contrast, cells reconstituted with the noncleavable mutant of c-FLIPL (D376E) did not show processing to p43-FLIP (Figure 8E; Supplementary Figure S9). Noticeably, as postulated by the model, this resulted in a strong reduction of the levels of IκBα phosphorylation on CD95 stimulation. Hence, by perturbing the ratio of procaspase-8 to c-FLIPL at the DISC, we directed the induction of apoptosis and NF-κB activation as predicted by our model. Taken together, we found that the DISC protein levels determine cell fate in a nonlinear manner, highlighting the role of signal processing within the DISC.

In this study, we propose, to the best of our knowledge, the first integrated kinetic model of CD95-mediated apoptosis and NF-κB signaling. This was achieved by integrating mechanistic knowledge of DISC assembly and caspase activation with a simple scheme of NF-κB activation. We observed that c-FLIPL levels crucially determine the balance between apoptotic and NF-κB signaling by shaping the dynamics of DISC assembly. Although this finding is based on experiments performed in cell lines, we expect that the nonlinear dynamics of DISC assembly is a generic systems property of life/death decision making in CD95 signaling pathways. This is especially important for understanding the regulation of cell death in physiologically relevant cells, such as cancer cells often showing resistance against death receptor-induced apoptosis.

This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-κB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-κB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIPL cleavage in the death-inducing signaling complex (DISC) and the NF-κB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-κB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIPL and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-κB signaling.

Background

We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.

Results

We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity.

Conclusions

The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.

Assembly and release of human immunodeficiency virus (HIV) occur at the plasma membrane of infected cells and are driven by the Gag polyprotein. Previous studies analyzed viral morphogenesis using biochemical methods and static images, while dynamic and kinetic information has been lacking until very recently. Using a combination of wide-field and total internal reflection fluorescence microscopy, we have investigated the assembly and release of fluorescently labeled HIV-1 at the plasma membrane of living cells with high time resolution. Gag assembled into discrete clusters corresponding to single virions. Formation of multiple particles from the same site was rarely observed. Using a photoconvertible fluorescent protein fused to Gag, we determined that assembly was nucleated preferentially by Gag molecules that had recently attached to the plasma membrane or arrived directly from the cytosol. Both membrane-bound and cytosol derived Gag polyproteins contributed to the growing bud. After their initial appearance, assembly sites accumulated at the plasma membrane of individual cells over 1–2 hours. Assembly kinetics were rapid: the number of Gag molecules at a budding site increased, following a saturating exponential with a rate constant of ∼5×10−3 s−1, corresponding to 8–9 min for 90% completion of assembly for a single virion. Release of extracellular particles was observed at ∼1,500±700 s after the onset of assembly. The ability of the virus to recruit components of the cellular ESCRT machinery or to undergo proteolytic maturation, or the absence of Vpu did not significantly alter the assembly kinetics.

Author Summary

Human immunodeficiency virus (HIV) particles are formed and released at the plasma membrane of the infected cell. Here, we analyzed the dynamics of HIV assembly and release making use of fluorescently labeled HIV structural proteins. We determined that assembly of the viral protein shell occurs within ∼8–9 min after nucleation of an assembly site and virus particles are formed individually and not from large patches. Virion release was observed ∼25 min after nucleation of the assembly site. Assembly of the Gag shell thus appears to constitute only a minor part of the period required for particle formation indicating that traversing the membrane and fission are the rate-limiting stages in virion formation. Using a photoconvertible label in the viral Gag protein, we established that the Gag molecules driving nucleation of a new assembly site and in bud growth are recruited preferentially from the cytosolic pool of Gag molecules and from recently membrane-attached Gag. No intracellular assembly or vesicular trafficking of Gag was observed. The described results add essential dynamic information to our picture of virus release and provide an experimental basis for interfering with this stage of virus replication.