On the acquisition of +1 charge states during high-throughput proteomics: Implications on reproducibility, number and confidence of protein identifications

Modern high-throughput methods for the proteome analysis are gradually replacing more traditional 2D gel-based techniques. Almost immediately after the introduction of high-throughput proteomics techniques in 2001, reproducibility of the results became an issue. Extensive discussion in the literature led to the conclusion that certain "undersampling" exhibited during measurements could be due to the stochastic nature of the data-dependent sampling, routinely used with current mass spectrometry equipment. At the same time, the effect of the acquisition of different charge states on the reproducibility and confidence of protein identifications, to the best of our knowledge, has never been properly evaluated. There exists the frequently voiced yet hardly documented opinion that +1 charge states should be rejected during data-dependent acquisition. The work presented here shows that inclusion of the +1 charge state in the data-dependent acquisition protocols can indeed lead to improved proteome coverage, reproducibility, and the confidence of protein identifications by high-throughput proteomics. It was also shown that contrary to the established opinion, gas-phase dissociation of singly charged peptide species results in rich fragmentation patterns containing both b- and y-ions allowing for successful and confident peptide identification.

Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae

Background

Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism.

Results

Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism.

Conclusion

Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.

Connecting extracellular metabolomic measurements to intracellular flux states in yeast

Background

Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results

We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The iMM904 metabolic network was reconstructed based on an existing genome-scale network, iND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the iMM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed.

Conclusion

Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states.

Applications of metabolomics and proteomics to the mdx mouse model of Duchenne muscular dystrophy: lessons from downstream of the transcriptome

Functional genomic studies are dominated by transcriptomic approaches, in part reflecting the vast amount of information that can be obtained, the ability to amplify mRNA and the availability of commercially standardized functional genomic DNA microarrays and related techniques. This can be contrasted with proteomics, metabolomics and metabolic flux analysis (fluxomics), which have all been much slower in development, despite these techniques each providing a unique viewpoint of what is happening in the overall biological system. Here, we give an overview of developments in these fields 'downstream' of the transcriptome by considering the characterization of one particular, but widely used, mouse model of human disease. The mdx mouse is a model of Duchenne muscular dystrophy (DMD) and has been widely used to understand the progressive skeletal muscle wasting that accompanies DMD, and more recently the associated cardiomyopathy, as well as to unravel the roles of the other isoforms of dystrophin, such as those found in the brain. Studies using proteomics, metabolomics and fluxomics have characterized perturbations in calcium homeostasis in dystrophic skeletal muscle, provided an understanding of the role of dystrophin in skeletal muscle regeneration, and defined the changes in substrate energy metabolism in the working heart. More importantly, they all point to perturbations in proteins, metabolites and metabolic fluxes reflecting mitochondrial energetic alterations, even in the early stage of the dystrophic pathology. Philosophically, these studies also illustrate an important lesson relevant to both functional genomics and the mouse phenotyping in that the knowledge generated has advanced our understanding of cell biology and physiological organization as much as it has advanced our understanding of the disease.

Transcription and splicing regulation in human umbilical vein endothelial cells under hypoxic stress conditions by exon array

Background

The balance between endothelial cell survival and apoptosis during stress is an important cellular process for vessel integrity and vascular homeostasis, and it is also pivotal in angiogenesis during the development of many vascular diseases. However, the underlying molecular mechanisms remain largely unknown. Although both transcription and alternative splicing are important in regulating gene expression in endothelial cells under stress, the regulatory mechanisms underlying this state and their interactions have not yet been studied on a genome-wide basis.

Results

Human umbilical vein endothelial cells (HUVECs) were treated with cobalt chloride (CoCl₂) both to mimic hypoxia and to induce cell apoptosis and alternative splicing responses. Cell apoptosis rate analysis indicated that HUVECs exposed to 300 μM CoCl₂ for 24 hrs were initially counterbalancing apoptosis with cell survival. We therefore used the Affymetrix exon array system to determine genome-wide transcript- and exon-level differential expression. Other than 1583 differentially expressed transcripts, 342 alternatively spliced exons were detected and classified by different splicing types. Sixteen alternatively spliced exons were validated by RT-PCR. Furthermore, direct evidence for the ongoing balance between HUVEC survival and apoptosis was provided by Gene Ontology (GO) and protein function, as well as protein domain and pathway enrichment analyses of the differentially expressed transcripts. Importantly, a novel molecular module, in which the heat shock protein (HSP) families play a significant role, was found to be activated under mimicked hypoxia conditions. In addition, 46% of the transcripts containing stress-modulated exons were differentially expressed, indicating the possibility of combinatorial regulation of transcription and splicing.

Conclusion

The exon array system effectively profiles gene expression and splicing on the genome-wide scale. Based on this approach, our data suggest that transcription and splicing not only regulate gene expression, but also carry out combinational regulation of the balance between survival and apoptosis of HUVECs under mimicked hypoxia conditions. Since cell survival following the apoptotic challenge is pivotal in angiogenesis during the development of many vascular diseases, our results may advance the knowledge of multilevel gene regulation in endothelial cells under physiological and pathological conditions.

Diversity in bacterial chemotactic responses and niche adaptation

The ability of microbes to rapidly sense and adapt to environmental changes plays a major role in structuring microbial communities, in affecting microbial activities, as well as in influencing various microbial interactions with the surroundings. The bacterial chemotaxis signal transduction system is the sensory perception system that allows motile cells to respond optimally to changes in environmental conditions by allowing cells to navigate in gradients of diverse physicochemical parameters that can affect their metabolism. The analysis of complete genome sequences from microorganisms that occupy diverse ecological niches reveal the presence of multiple chemotaxis pathways and a great diversity of chemoreceptors with novel sensory specificities. Owing to its role in mediating rapid responses of bacteria to changes in the surroundings, bacterial chemotaxis is a behavior of interest in applied microbiology as it offers a unique opportunity for understanding the environmental cues that contribute to the survival of bacteria. This chapter explores the diversity of bacterial chemotaxis and suggests how gaining further insights into such diversity may potentially impact future drug and pesticides development and could inform bioremediation strategies.

Bioanalysis of recombinant proteins and antibodies by mass spectrometry

In recent years, biotechnologically-derived drugs have been a major focus of research and development in the pharmaceutical industry. Their pharmacokinetics and pharmacokinetic/pharmacodynamic relationships impact every stage of the development process and require their assessment in the circulation in preclinical species and in humans. To this end, immunoassays are a reference, but standardisation remains an issue owing to the restricted pattern of antibody specificity and interference with endogenous components. As an alternative, we report here analytical strategies involving liquid chromatography coupled to mass spectrometry (LC-MS) for the accurate quantification of therapeutic proteins and antibodies in biological fluids.

Molecular evolution of the junctophilin gene family

Junctophilins (JPHs) are members of a junctional membrane complex protein family important for the physical approximation of plasmalemmal and sarcoplasmic/endoplasmic reticulum membranes. As such, JPHs facilitate signal transduction in excitable cells between plasmalemmal voltage-gated calcium channels and intracellular calcium release channels. To determine the molecular evolution of the JPH gene family, we performed a phylogenetic analysis of over 60 JPH genes from over 40 species and compared conservation across species and different isoforms. We found that JPHs are evolutionary highly conserved, in particular the membrane occupation and recognition nexus motifs found in all species. Our data suggest that an ancestral form of JPH arose at the latest in a common metazoan ancestor and that in vertebrates four isoforms arose, probably following two rounds of whole genome duplications. By combining multiple prediction techniques with sequence alignments, we also postulate the presence of new important functional regions and candidate sites for posttranslational modifications. The increasing number of available sequences yields significant insight into the molecular evolution of JPHs. Our analysis is consistent with the emerging concept that JPHs serve dual important functions in excitable cells: structural assembly of junctional membrane complexes and regulation of intracellular calcium signaling pathways.

The chromosomal toxin gene yafQ is a determinant of multidrug tolerance for Escherichia coli growing in a biofilm

Escherichia coli is refractory to elevated doses of antibiotics when it is growing in a biofilm, and this is potentially due to high numbers of multidrug-tolerant persister cells in the surface-adherent population. Previously, the chromosomal toxin-antitoxin loci hipBA and relBE have been linked to the frequency at which persister cells occur in E. coli populations. In the present study, we focused on the dinJ-yafQ-encoded toxin-antitoxin system and hypothesized that deletion of the toxin gene yafQ might influence cell survival in antibiotic-exposed biofilms. By using confocal laser scanning microscopy and viable cell counting, it was determined that a Delta yafQ mutant produced biofilms with a structure and a cell density equivalent to those of the parental strain. In-depth susceptibility testing identified that relative to wild-type E. coli, the Delta yafQ strain had up to a approximately 2,400-fold decrease in cell survival after the biofilms were exposed to bactericidal concentrations of cefazolin or tobramycin. Corresponding to these data, controlled overexpression of yafQ from a high-copy-number plasmid resulted in up to a approximately 10,000-fold increase in the number of biofilm cells surviving exposure to these bactericidal drugs. In contrast, neither the inactivation nor the overexpression of yafQ affected the tolerance of biofilms to doxycycline or rifampin (rifampicin). Furthermore, deletion of yafQ did not affect the tolerance of stationary-phase planktonic cells to any of the antibacterials tested. These results suggest that yafQ mediates the tolerance of E. coli biofilms to multiple but specific antibiotics; moreover, our data imply that this cellular pathway for persistence is likely different from that of multidrug-tolerant cells in stationary-phase planktonic cell cultures.

Identifying disease-specific genes based on their topological significance in protein networks

BACKGROUND

The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest.

RESULTS

In this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis.

CONCLUSION

The systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking

BACKGROUND

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

METHODOLOGY/PRINCIPAL FINDINGS

This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.

CONCLUSIONS/SIGNIFICANCE

In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically nulls. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages.

Let-7 and miR-200 microRNAs: guardians against pluripotency and cancer progression

Micro (mi)RNAs are emerging as important regulators of cellular differentiation, their importance underscored by the fact that they are often dysregulated during carcinogenesis. Two evolutionary conserved families, let-7 and miR-200, regulate key differentiation processes during development. Loss of let-7 in cancer results in reverse embryogenesis and dedifferentiation, and miR-200 has been identified as a powerful regulator of epithelial-to-mesenchymal transition (EMT). Recent findings have connected let-7 with stem cell maintenance and point at a connection between EMT and stem cell formation. A part of tumor progression can be viewed as a continuum of progressive dedifferentiation (EMT) with a cell at the endpoint that has stem cell-like properties. I propose that steps of this process are driven by specific changes in the expression of let-7 and miR-200 family members.

Identification of a lipid kinase as a host factor involved in hepatitis C virus RNA replication

A functional screen of an adenovirus-delivered shRNA library that targets approximately 4500 host genes was performed to identify cellular factors that regulate hepatitis C virus (HCV) sub-genomic RNA replication. Seventy-three hits were further examined by siRNA oligonucleotide-directed knockdown, and silencing of the PI4KA gene was demonstrated to have a significant effect on the replication of a HCV genotype 1b replicon. Using transient siRNA oligonucleotide transfections and stable shRNA knockdown clones in HuH-7 cells, the PI4KA gene was shown to be essential for the replication of all HCV genotypes tested (1a, 1b and 2a) but not required for bovine viral diarrhea virus (BVDV) RNA replication.

Measuring the activity of BioBrick promoters using an in vivo reference standard

Background

The engineering of many-component, synthetic biological systems is being made easier by the development of collections of reusable, standard biological parts. However, the complexity of biology makes it difficult to predict the extent to which such efforts will succeed. As a first practical example, the Registry of Standard Biological Parts started at MIT now maintains and distributes thousands of BioBrick™ standard biological parts. However, BioBrick parts are only standardized in terms of how individual parts are physically assembled into multi-component systems, and most parts remain uncharacterized. Standardized tools, techniques, and units of measurement are needed to facilitate the characterization and reuse of parts by independent researchers across many laboratories.

Results

We found that the absolute activity of BioBrick promoters varies across experimental conditions and measurement instruments. We choose one promoter (BBa_J23101) to serve as an in vivo reference standard for promoter activity. We demonstrated that, by measuring the activity of promoters relative to BBa_J23101, we could reduce variation in reported promoter activity due to differences in test conditions and measurement instruments by ~50%. We defined a Relative Promoter Unit (RPU) in order to report promoter characterization data in compatible units and developed a measurement kit so that researchers might more easily adopt RPU as a standard unit for reporting promoter activity. We distributed a set of test promoters to multiple labs and found good agreement in the reported relative activities of promoters so measured. We also characterized the relative activities of a reference collection of BioBrick promoters in order to further support adoption of RPU-based measurement standards.

Conclusion

Relative activity measurements based on an in vivoreference standard enables improved measurement of promoter activity given variation in measurement conditions and instruments. These improvements are sufficient to begin to support the measurement of promoter activities across many laboratories. Additional in vivo reference standards for other types of biological functions would seem likely to have similar utility, and could thus improve research on the design, production, and reuse of standard biological parts.

Revisiting the COP9 signalosome as a transcriptional regulator

The COP9 signalosome (CSN) is a highly conserved protein complex that was originally described as a repressor of light-dependent growth and transcription in Arabidopsis. The most studied CSN function is the regulation of protein degradation, which occurs primarily through the removal of the ubiquitin-like modifier Nedd8 from cullin-based E3 ubiquitin ligases. This activity can regulate transcription-factor stability and, therefore, transcriptional activity. Recent data suggest that the CSN also regulates transcription on the chromatin by mechanisms that are not yet clearly understood. Furthermore, the CSN subunits CSN5 and CSN2 seem to act as transcriptional coactivators and corepressors, respectively. Here, I re-evaluate the mechanisms by which the CSN acts as a transcriptional regulator, and suggest that they could extend beyond the regulation of protein stability.

From damaged genome to cell surface: transcriptome changes during bacterial cell death triggered by loss of a restriction-modification gene complex

Genetically programmed cell deaths play important roles in unicellular prokaryotes. In postsegregational killing, loss of a gene complex from a cell leads to its descendants' deaths. With type II restriction-modification gene complexes, such death is triggered by restriction endonuclease's attacks on under-methylated chromosomes. Here, we examined how the Escherichia coli transcriptome changes after loss of PaeR7I gene complex. At earlier time points, activation of SOS genes and sigma(E)-regulon was noticeable. With time, more SOS genes, stress-response genes (including sigma(S)-regulon, osmotic-, oxidative- and periplasmic-stress genes), biofilm-related genes, and many hitherto uncharacterized genes were induced, and genes for energy metabolism, motility and outer membrane biogenesis were repressed. As expected from the activation of sigma(E)-regulon, the death was accompanied by cell lysis and release of cellular proteins. Expression of several sigma(E)-regulon genes indeed led to cell lysis. We hypothesize that some signal was transduced, among multiple genes involved, from the damaged genome to the cell surface and led to its disintegration. These results are discussed in comparison with other forms of programmed deaths in bacteria and eukaryotes.

An automated GCxGC-TOF-MS protocol for batch-wise extraction and alignment of mass isotopomer matrixes from differential 13C-labelling experiments: a case study for photoautotrophic-mixotrophic grown Chlamydomonas reinhardtii cells

Two dimensional gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOF-MS) is a promising technique to overcome limits of complex metabolome analysis using one dimensional GC-TOF-MS. Especially at the stage of data export and data mining, however, convenient procedures to cope with the complexity of GCxGC-TOF-MS data are still in development. Here, we present a high sample throughput protocol exploiting first and second retention index for spectral library search and subsequent construction of a high dimensional data matrix useful for statistical analysis. The method was applied to the analysis of (13)C-labelling experiments in the unicellular green alga Chlamydomonas reinhardtii. We developed a rapid sampling and extraction procedure for Chlamydomonas reinhardtii laboratory strain (CC503), a cell wall deficient mutant. By testing all published quenching protocols we observed dramatic metabolite leakage rates for certain metabolites. To circumvent metabolite leakage, samples were directly quenched and analyzed without separation of the medium. The growth medium was adapted to this rapid sampling protocol to avoid interference with GCxGC-TOF-MS analysis. To analyse batches of samples a new software tool, MetMax, was implemented which extracts the isotopomer matrix from stable isotope labelling experiments together with the first and second retention index (RI1 and RI2). To exploit RI1 and RI2 for metabolite identification we used the Golm metabolome database (GMD [1] with RI1/RI2-reference spectra and new search algorithms. Using those techniques we analysed the dynamics of (13)CO(2) and (13)C-acetate uptake in Chlamydomonas reinhardtii cells in two different steady states namely photoautotroph and mixotroph growth conditions.

Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis

Mitochondria are central to many cellular processes including respiration, ion homeostasis, and apoptosis. Using computational predictions combined with traditional quantitative experiments, we have identified 100 proteins whose deficiency alters mitochondrial biogenesis and inheritance in Saccharomyces cerevisiae. In addition, we used computational predictions to perform targeted double-mutant analysis detecting another nine genes with synthetic defects in mitochondrial biogenesis. This represents an increase of about 25% over previously known participants. Nearly half of these newly characterized proteins are conserved in mammals, including several orthologs known to be involved in human disease. Mutations in many of these genes demonstrate statistically significant mitochondrial transmission phenotypes more subtle than could be detected by traditional genetic screens or high-throughput techniques, and 47 have not been previously localized to mitochondria. We further characterized a subset of these genes using growth profiling and dual immunofluorescence, which identified genes specifically required for aerobic respiration and an uncharacterized cytoplasmic protein required for normal mitochondrial motility. Our results demonstrate that by leveraging computational analysis to direct quantitative experimental assays, we have characterized mutants with subtle mitochondrial defects whose phenotypes were undetected by high-throughput methods.

Mitochondria are the proverbial powerhouses of the cell, running the fundamental biochemical processes that produce energy from nutrients using oxygen. These processes are conserved in all eukaryotes, from humans to model organisms such as baker's yeast. In humans, mitochondrial dysfunction plays a role in a variety of diseases, including diabetes, neuromuscular disorders, and aging. In order to better understand fundamental mitochondrial biology, we studied genes involved in mitochondrial biogenesis in the yeast S. cerevisiae, discovering over 100 proteins with novel roles in this process. These experiments assigned function to 5% of the genes whose function was not known. In order to achieve this rapid rate of discovery, we developed a system incorporating highly quantitative experimental assays and an integrated, iterative process of computational protein function prediction. Beginning from relatively little prior knowledge, we found that computational predictions achieved about 60% accuracy and rapidly guided our laboratory work towards hundreds of promising candidate genes. Thus, in addition to providing a more thorough understanding of mitochondrial biology, this study establishes a framework for successfully integrating computation and experimentation to drive biological discovery. A companion manuscript, published in PLoS Computational Biology (doi:10.1371/journal.pcbi.1000322), discusses observations and conclusions important for the computational community.

Multifunctionality dominantly determines the rate of human housekeeping and tissue specific interacting protein evolution

Elucidation of the determinants of the rate of protein sequence evolution is one of the great challenges in evolutionary biology. It has been proposed that housekeeping genes are evolutionarily slower than tissue specific genes. In the present communication, we have examined different determinants that influence the evolutionary rate variation in human housekeeping and tissue specific proteins present in protein-protein interaction network. Studies on yeast proteome, revealed a predominant role of protein connectivity in determining the rate of protein evolution. However, in human, we did not observe any significant influence of protein connectivity on its evolutionary rate. Rather, a significant impact of the proportion of protein's interacting length (amount of protein interface involved in interaction with its partners), expression level and multifunctionality has been observed in determining the rate of protein evolution. We also observed that multi interface proteins are evolutionarily conserved between housekeeping and tissue specific genes and it has been found that the average number of biological processes they associated in these two sets of genes is similar. Moreover, single interface proteins in housekeeping genes evolve more slowly as compared to tissue specific genes owing to their involvement in different number of biological processes. Partial correlation analysis suggests that the relative importance of three individual factors in determining the evolutionary rate variation between housekeeping and tissue specific proteins is in the order of protein multifunctionality>protein expression level>interacting protein length.

Systems biology evaluation of immune responses induced by human host defence peptide LL-37 in mononuclear cells

The immune system is very complex, it involves the integrated regulation and expression of hundreds of proteins. To understand in greater detail how the human host defence immunomodulatory peptide LL-37 interacts with innate immunity, a systems approach was pursued. Polychromatic flow cytometry was employed to demonstrate that within human peripheral blood mononuclear cells, CD14+ monocytes, myeloid and plasmocytoid dendritic cells and T- and B-lymphocytes, all responded to LL-37, with the differential production of intracellular cytokines. Microarray analyses with CD14+ monocytes indicated the differential expression of 475 genes in response to stimulation with LL-37. To understand this complex response, bioinformatic interrogation, using InnateDB, of the gene ontology, signalling pathways and transcription factor binding sites was undertaken. Activation of the IkappaBalpha/NFkappaB, mitogen-activated protein kinases p38, ERK1/2 and JNK, and PI3K signalling pathways in response to LL-37 was demonstrated by pathway and ontology over-representation analyses, and confirmed experimentally by inhibitor studies. Computational analysis of the predicted transcription factor binding sites upstream of the genes that were regulated by LL-37 predicted the involvement of several transcription factors including NFkappaB and five novel factors, AP-1, AP-2, SP-1, E2F1, and EGR, which were experimentally confirmed to respond to LL-37 by performing transcription factor array studies on nuclear extracts from LL-37 treated mononuclear cells. These data are discussed as reflecting the integration of several responsive signalling pathways through the involvement of transcription factor complexes in gene expression activated by LL-37 in human mononuclear cells.

Information processing in the transcriptional regulatory network of yeast: functional robustness

Background

Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism.

Results

In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value.

Conclusion

Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties.

Metabolomics tools for identifying biomarkers for neuropsychiatric diseases

The repertoire of biochemicals (or small molecules) present in cells, tissue, and body fluids is known as the metabolome. Today, clinicians utilize only a very small part of the information contained in the metabolome, as revealed by the quantification of a limited set of analytes to gain information on human health. Examples include measuring glucose or cholesterol to monitor diabetes and cardiovascular health, respectively. With a focus on comprehensively studying the metabolome, the rapidly growing field of metabolomics captures the metabolic state of organisms at the global or "-omics" level. Given that the overall health status of an individual is captured by his or her metabolic state, which is a reflection of what has been encoded by the genome and modified by environmental factors, metabolomics has the potential to have a great impact upon medical practice by providing a wealth of relevant biochemical data. Metabolomics promises to improve current, single metabolites-based clinical assessments by identifying metabolic signatures (biomarkers) that embody global biochemical changes in disease, predict responses to treatment or medication side effects (pharmachometabolomics). State of the art metabolomic analytical platforms and informatics tools are being used to map potential biomarkers for a multitude of disorders including those of the central nervous system (CNS). Indeed, CNS disorders are linked to disturbances in metabolic pathways related to neurotransmitter systems (dopamine, serotonin, GABA and glutamate); fatty acids such as arachidonic acid-cascade; oxidative stress and mitochondrial function. Metabolomics tools are enabling us to map in greater detail perturbations in many biochemical pathways and links among these pathways this information is key for development of biomarkers that are disease-specific. In this review, we elaborate on some of the concepts and technologies used in metabolomics and its promise for biomarker discovery. We also highlight early findings from metabolomic studies in CNS disorders such as schizophrenia, Major Depressive Disorder (MDD), Bipolar Disorder (BD), Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).

Current concepts in research related to oncogenes implicated in salivary gland tumourigenesis: a review of the literature

BACKGROUND

Salivary gland tumours are relatively uncommon and there exists considerable difficulty in decisions regarding prognosis and management, as well as diagnostic uncertainty that has implications for treatment.

METHOD

Literature pertaining to individual oncogenes has been reviewed and commented upon, specifically looking at the role of these as diagnostic and prognostic markers and as potential targets for treatments.

RESULTS

kit, PLAG1, Mect1-Maml2, HMGIC, HER2/neu, ras, c-fos and Sox-4 all have seminal small-scale studies in the literature with potential for further research and eventual clinical applications.

CONCLUSION

A wide variety of oncogenes are implicated in salivary gland tumourigenesis, with evidence being confined to small murine or in vitro studies more often than not. There are possible roles for different oncogenes in therapeutics, prognosis and management of specific salivary gland tumours.

Nucleocytoplasmic transport: a thermodynamic mechanism

The nuclear pore supports molecular communication between cytoplasm and nucleus in eukaryotic cells. Selective transport of proteins is mediated by soluble receptors, whose regulation by the small GTPase Ran leads to cargo accumulation in, or depletion from, the nucleus, i.e., nuclear import or nuclear export. We consider the operation of this transport system by a combined analytical and experimental approach. Provocative predictions of a simple model were tested using cell-free nuclei reconstituted in Xenopus egg extract, a system well suited to quantitative studies. We found that accumulation capacity is limited, so that introduction of one import cargo leads to egress of another. Clearly, the pore per se does not determine transport directionality. Moreover, different cargo reach a similar ratio of nuclear to cytoplasmic concentration in steady-state. The model shows that this ratio should in fact be independent of the receptor-cargo affinity, though kinetics may be strongly influenced. Numerical conservation of the system components highlights a conflict between the observations and the popular concept of transport cycles. We suggest that chemical partitioning provides a framework to understand the capacity to generate concentration gradients by equilibration of the receptor-cargo intermediary.

Osteoblast-induced EGFR/ERBB2 signaling in androgen-sensitive prostate carcinoma cells characterized by multiplex kinase activity profiling

Bone metastases in prostate cancer are predominantly osteoblastic. To study regulatory mechanisms underlying the establishment of prostate cancer within an osteoblastic microenvironment, human androgen-sensitive prostate carcinoma cells (LNCaP) were treated with culture medium conditioned by human osteoblast-derived sarcoma cells (OHS), and activated signalling pathways in the carcinoma cells were analyzed using microarrays with tyrosine kinase substrates. Network interaction analysis of substrates with significantly increased phosphorylation levels revealed that signalling pathways mediated by EGFR and ERBB2 were activated in LNCaP cells under OHS influence but also by androgen treatment. Activation of EGFR/ERBB2 signalling was also found in LNCaP cells in cocultures with OHS cells or osteoblastic cells that had been differentiated from human mesenchymal stem cells. Our experimental data suggests osteoblast-directed induction of signalling activity via EGFR and ERBB2 in prostate carcinoma cells and may provide a rationale for the use of EGFR or ERBB2 inhibition in systemic prevention or treatment of metastatic prostate cancer in the androgen-sensitive stage of the disease.

Synergy Disequilibrium Plots: graphical visualization of pairwise synergies and redundancies of SNPs with respect to a phenotype

Summary:We present a visualization tool applied on genome-wide association data, revealing disease-associated haplotypes, epistatically interacting loci, as well as providing visual signatures of multivariate correlations of genetic markers with respect to a phenotype.

Availability:Freely available on the web at: http://www.ee.columbia.edu/~anastas/sdplots

Contact:anastas@ee.columbia.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Temporal switching and cell-to-cell variability in Ca2+ release activity in mammalian cells

Genetically identical cells in a uniform external environment can exhibit different phenotypes, which are often masked by conventional measurements that average over cell populations. Although most studies on this topic have used microorganisms, differentiated mammalian cells have rarely been explored. Here, we report that only approximately 40% of clonal human embryonic kidney 293 cells respond with an intracellular Ca²⁺ increase when ryanodine receptor Ca²⁺ release channels in the endoplasmic reticulum are maximally activated by caffeine. On the other hand, the expression levels of ryanodine receptor showed a unimodal distribution. We showed that the difference in the caffeine sensitivity depends on a critical balance between Ca²⁺ release and Ca²⁺ uptake activities, which is amplified by the regenerative nature of the Ca²⁺ release mechanism. Furthermore, individual cells switched between the caffeine-sensitive and caffeine-insensitive states with an average transition time of approximately 65 h, suggestive of temporal fluctuation in endogenous protein expression levels associated with caffeine response. These results suggest the significance of regenerative mechanisms that amplify protein expression noise and induce cell-to-cell phenotypic variation in mammalian cells.

Systems biological analysis of epidermal growth factor receptor internalization dynamics for altered receptor levels

Epidermal growth factor (EGF) receptor (EGFR) overexpression is a hallmark of many cancers. EGFR endocytosis is a critical step in signal attenuation, raising the question of how receptor expression levels affect the internalization process. Here we combined quantitative experimental and mathematical modeling approaches to investigate the role of the EGFR expression level on the rate of receptor internalization. Using tetramethylrhodamine-labeled EGF, we established assays for quantifying EGF-triggered EGFR internalization by both high resolution confocal microscopy and flow cytometry. We determined that the flow cytometry approach was more sensitive for examining large populations of cells. Mathematical modeling was used to investigate the relationship between EGF internalization kinetics, EGFR expression, and internalization machinery. We predicted that the standard parameter used to assess internalization kinetics, the temporal evolution r(t) of the ratio of internalized versus surface-located ligand.receptor complexes, does not describe a straight line, as proposed previously. Instead, a convex or concave curve occurs depending on whether initial receptor numbers or internalization adaptors are limiting the uptake reaction, respectively. To test model predictions, we measured EGF-EGFR binding and internalization in cells expressing different levels of green fluorescent protein-EGFR. As expected, surface binding of rhodamine-labeled EGF increased with green fluorescent protein-EGFR expression level. Unexpectedly, internalization of ligand. receptor complexes increased linearly with increasing receptor expression level, suggesting that receptors and not internalization adaptors were limiting the uptake in our experimental model. Finally, determining the ratio of internalized versus surface-located ligand.receptor complexes for this cell line confirmed that it follows a convex curve, supporting our model predictions.

Complex topology rather than complex membership is a determinant of protein dosage sensitivity

The ‘balance hypothesis' predicts that non-stoichiometric variations in concentrations of proteins participating in complexes should be deleterious. As a corollary, heterozygous deletions and overexpression of protein complex members should have measurable fitness effects. However, genome-wide studies of heterozygous deletions in Saccharomyces cerevisiae and overexpression have been unable to unambiguously relate complex membership to dosage sensitivity. We test the hypothesis that it is not complex membership alone but rather the topology of interactions within a complex that is a predictor of dosage sensitivity. We develop a model that uses the law of mass action to consider how complex formation might be affected by varying protein concentrations given a protein's topological positioning within the complex. Although we find little evidence for combinatorial inhibition of complex formation playing a major role in overexpression phenotypes, consistent with previous results, we show significant correlations between predicted sensitivity of complex formation to protein concentrations and both heterozygous deletion fitness and protein abundance noise levels. Our model suggests a mechanism for dosage sensitivity and provides testable predictions for the effect of alterations in protein abundance noise.

Toward accurate reconstruction of functional protein networks

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes/proteins in various cellular responses or functions. A fundamental challenge is to chart out the protein pathways that underlie these systems. Previous approaches to the problem have either employed a local optimization criterion, aiming to infer each pathway independently, or a global criterion, searching for the overall most parsimonious subnetwork. Here, we study the trade-off between the two approaches and present a new intermediary scheme that provides explicit control over it. We demonstrate its utility in the analysis of the apoptosis network in humans, and the telomere length maintenance (TLM) system in yeast. Our results show that in the majority of real-life cases, the intermediary approach provides the most plausible solutions. We use a new set of perturbation experiments measuring the role of essential genes in telomere length regulation to further study the TLM network. Surprisingly, we find that the proteasome plays an important role in telomere length regulation through its associations with transcription and DNA repair circuits.

Developmental and evolutionary basis for drought tolerance of the Anopheles gambiae embryo

During the evolution of the Diptera there is a dramatic modification of the embryonic ectoderm, whereby mosquitoes contain separate amnion and serosa lineages while higher flies such as Drosophila melanogaster contain a single amnioserosa. Whole-genome transcriptome assays were performed with isolated serosa from Anopheles gambiae embryos. These assays identified a large number of genes implicated in the production of the larval cuticle. In D. melanogaster, these genes are activated just once during embryogenesis, during late stages where they are used for the production of the larval cuticle. Evidence is presented that the serosal cells secrete a dedicated serosal cuticle, which protects A. gambiae embryos from desiccation. Detailed temporal microarray assays of mosquito gene expression profiles revealed that the cuticular genes display biphasic expression during A. gambiae embryogenesis, first in the serosa of early embryos and then again during late stages as seen in D. melanogaster. We discuss how evolutionary modifications in the well-defined dorsal-ventral patterning network led to the wholesale deployment of the cuticle biosynthesis pathway in early embryos of A. gambiae.

Force- and kinesin-8-dependent effects in the spatial regulation of fission yeast microtubule dynamics

Microtubules (MTs) are central to the organisation of the eukaryotic intracellular space and are involved in the control of cell morphology. For these purposes, MT polymerisation dynamics are tightly regulated. Using automated image analysis software, we investigate the spatial dependence of MT dynamics in interphase fission yeast cells with unprecedented statistical accuracy. We find that MT catastrophe frequencies (switches from polymerisation to depolymerisation) strongly depend on intracellular position. We provide evidence that compressive forces generated by MTs growing against the cell pole locally reduce MT growth velocities and enhance catastrophe frequencies. Furthermore, we find evidence for an MT length-dependent increase in the catastrophe frequency that is mediated by kinesin-8 proteins (Klp5/6). Given the intrinsic susceptibility of MT dynamics to compressive forces and the widespread importance of kinesin-8 proteins, we propose that similar spatial regulation of MT dynamics plays a role in other cell types as well. In addition, our systematic and quantitative data should provide valuable input for (mathematical) models of MT organisation in living cells.

Statistical identification of gene association by CID in application of constructing ER regulatory network

Background

A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).

Results

The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays.

Conclusion

CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers.

Availability

the implementation of CID in R codes can be freely downloaded from .

Reconstructing generalized logical networks of transcriptional regulation in mouse brain from temporal gene expression data

Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.

The photosynthetic apparatus and its regulation in the aerobic gammaproteobacterium Congregibacter litoralis gen. nov., sp. nov

BACKGROUND

There is accumulating evidence that in some marine environments aerobic bacteriochlorophyll a-producing bacteria represent a significant part of the microbial population. The interaction of photosynthesis and carbon metabolism in these interesting bacteria is still largely unknown and requires further investigation in order to estimate their contribution to the marine carbon cycle.

METHODOLOGY/PRINCIPAL FINDINGS

Here, we analyzed the structure, composition and regulation of the photosynthetic apparatus in the obligately aerobic marine gammaproteobacterium KT71(T). Photoheterotrophically grown cells were characterized by a poorly developed lamellar intracytoplasmic membrane system, a type 1 light-harvesting antenna complex and a photosynthetic reaction center associated with a tetraheme cytochrome c. The only photosynthetic pigments produced were bacteriochlorophyll a and spirilloxanthin. Under semiaerobic conditions KT71(T) cells expressing a photosynthetic apparatus showed a light-dependent increase of growth yield in the range of 1.3-2.5 fold. The expression level of the photosynthetic apparatus depended largely on the utilized substrate, the intermediary carbon metabolism and oxygen tension. In addition, pigment synthesis was strongly influenced by light, with blue light exerting the most significant effect, implicating that proteins containing a BLUF domain may be involved in regulation of the photosynthetic apparatus. Several phenotypic traits in KT71(T) could be identified that correlated with the assumed redox state of growing cells and thus could be used to monitor the cellular redox state under various incubation conditions.

CONCLUSIONS/SIGNIFICANCE

In a hypothetical model that explains the regulation of the photosynthetic apparatus in strain KT71(T) we propose that the expression of photosynthesis genes depends on the cellular redox state and is maximal under conditions that allow a balanced membrane redox state. So far, bacteria capable of an obligately aerobic, photosynthetic metabolism constitute a unique phenotype within the class Gammaproteobacteria, so that it is justified to propose a new genus and species, Congregibacter litoralis gen. nov, sp. nov., represented by the type strain KT71(T) ( = DSM 17192(T) = NBRC 104960(T)).

Regulation of clock-controlled genes in mammals

The complexity of tissue- and day time-specific regulation of thousands of clock-controlled genes (CCGs) suggests that many regulatory mechanisms contribute to the transcriptional output of the circadian clock. We aim to predict these mechanisms using a large scale promoter analysis of CCGs.

Our study is based on a meta-analysis of DNA-array data from rodent tissues. We searched in the promoter regions of 2065 CCGs for highly overrepresented transcription factor binding sites. In order to compensate the relatively high GC-content of CCG promoters, a novel background model to avoid a bias towards GC-rich motifs was employed. We found that many of the transcription factors with overrepresented binding sites in CCG promoters exhibit themselves circadian rhythms. Among the predicted factors are known regulators such as CLOCK∶BMAL1, DBP, HLF, E4BP4, CREB, RORα and the recently described regulators HSF1, STAT3, SP1 and HNF-4α. As additional promising candidates of circadian transcriptional regulators PAX-4, C/EBP, EVI-1, IRF, E2F, AP-1, HIF-1 and NF-Y were identified. Moreover, GC-rich motifs (SP1, EGR, ZF5, AP-2, WT1, NRF-1) and AT-rich motifs (MEF-2, HMGIY, HNF-1, OCT-1) are significantly overrepresented in promoter regions of CCGs. Putative tissue-specific binding sites such as HNF-3 for liver, NKX2.5 for heart or Myogenin for skeletal muscle were found. The regulation of the erythropoietin (Epo) gene was analysed, which exhibits many binding sites for circadian regulators. We provide experimental evidence for its circadian regulated expression in the adult murine kidney. Basing on a comprehensive literature search we integrate our predictions into a regulatory network of core clock and clock-controlled genes. Our large scale analysis of the CCG promoters reveals the complexity and extensiveness of the circadian regulation in mammals. Results of this study point to connections of the circadian clock to other functional systems including metabolism, endocrine regulation and pharmacokinetics.

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship.

Systems biology aims to understand the interactions of cellular components in a systemic manner. Mathematical modeling is critical to the integration and analysis of these components on a conceptual as well as mechanistic level. To date, detailed genome-scale reconstructions of metabolism have become available for a growing number of organisms. Although metabolism has an important role in cells, other cellular functions need to be considered as well, such as signaling, regulation, and macromolecular synthesis. For instance, the cellular machinery required for RNA and protein synthesis consists of a complex set of proteins. Here, we show that one can collect all of the necessary information for a prokaryotic organism to create a gene-specific, fine-grained representation of the macromolecular synthesis machinery. E. coli was chosen as a model organism because of the wealth of available information. The explicit representation of transcription and translation in terms of a mass-balanced network enables a detailed, quantitative accounting of the protein synthesis capabilities of E. coli in silico. Hence, this study demonstrates the feasibility of constructing very large networks and also represents a critical step toward building cellular models of growth that can account for gene-specific protein production in a stoichiometric fashion on the genome scale.

Pathway and network-based analysis of genome-wide association studies in multiple sclerosis

Genome-wide association studies (GWAS) testing several hundred thousand SNPs have been performed in multiple sclerosis (MS) and other complex diseases. Typically, the number of markers in which the evidence for association exceeds the genome-wide significance threshold is very small, and markers that do not exceed this threshold are generally neglected. Classical statistical analysis of these datasets in MS revealed genes with known immunological functions. However, many of the markers showing modest association may represent false negatives. We hypothesize that certain combinations of genes flagged by these markers can be identified if they belong to a common biological pathway. Here we conduct a pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05). Gene-wise P-values were superimposed on a human protein interaction network and searches were conducted to identify sub-networks containing a higher proportion of genes associated with MS than expected by chance. These sub-networks, and others generated at random as a control, were categorized for membership of biological pathways. GWAS from eight other diseases were analyzed to assess the specificity of the pathways identified. In the MS datasets, we identified sub-networks of genes from several immunological pathways including cell adhesion, communication and signaling. Remarkably, neural pathways, namely axon-guidance and synaptic potentiation, were also over-represented in MS. In addition to the immunological pathways previously identified, we report here for the first time the potential involvement of neural pathways in MS susceptibility.

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.