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1.  Salmonella Modulates Metabolism during Growth under Conditions that Induce Expression of Virulence Genes 
Molecular bioSystems  2013;9(6):1522-1534.
Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.
doi:10.1039/c3mb25598k
PMCID: PMC3665296  PMID: 23559334
2.  A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments 
Proteomics  2012;13(0):493-503.
Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g., disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years many computational approaches to compute relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to compute relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.
doi:10.1002/pmic.201200269
PMCID: PMC3775642  PMID: 23019139
label-free; peak intensity; protein quantification; relative
3.  A mutli-omic systems approach to elucidating Yersinia virulence mechanisms 
Molecular bioSystems  2012;9(1):44-54.
The underlying mechanisms that lead to dramatic differences between closely related pathogens are not always readily apparent. For example, the genomes of Yersinia pestis (YP) the causative agent of plague with a high mortality rate and Yersinia pseudotuberculosis (YPT) an enteric pathogen with a modest mortality rate are highly similar with some species specific differences; however the molecular causes of their distinct clinical outcomes remain poorly understood. In this study, a temporal multi-omic analysis of YP and YPT at physiologically relevant temperatures was performed to gain insights into how an acute and highly lethal bacterial pathogen, YP, differs from its less virulent progenitor, YPT. This analysis revealed higher gene and protein expression levels of conserved major virulence factors in YP relative to YPT, including the Yop virulon and the pH6 antigen. This suggests that adaptation in the regulatory architecture, in addition to the presence of unique genetic material, may contribute to the increased pathogenenicity of YP relative to YPT. Additionally, global transcriptome and proteome responses of YP and YPT revealed conserved post-transcriptional control of metabolism and the translational machinery including the modulation of glutamate levels in Yersiniae. Finally, the omics data was coupled with a computational network analysis, allowing an efficient prediction of novel Yersinia virulence factors based on gene and protein expression patterns.
doi:10.1039/c2mb25287b
PMCID: PMC3518462  PMID: 23147219
4.  An adaptive coarse graining method for signal transduction in three dimensions 
Fundamenta informaticae  2012;118(4):10.3233/FI-2012-720.
The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns. Building upon our earlier studies, we have developed an adaptive coarse-grained Monte Carlo method that can be used to investigate the role of diffusion, clustering and membrane corralling on receptor association and receptor-adaptor protein complex formation dynamics in three dimensions. The new Monte Carlo lattice based approach allowed us to introduce spatial resolution on the 2-D plasma membrane and to model the cytoplasm in three-dimensions. Being a multi-resolution approach, our new method makes it possible to represent various parts of the cellular system at different levels of detail and enabled us to utilize the locally homogeneous assumption when justified (e.g., cytoplasmic region away from the cell membrane) and avoid its use when high spatial resolution is needed (e.g., cell membrane and cytoplasmic region near the membrane) while keeping the required computational complexity manageable. Our results have shown that diffusion has a significant impact on receptor-receptor dimerization and receptor-adaptor protein complex formation kinetics. We have observed an “adaptor protein hopping” mechanism where the receptor binding proteins may hop between receptors to form short-lived transient complexes. This increased residence time of the adaptor proteins near cell membrane and their ability to frequently change signaling partners may explain the increase in signaling efficiency when receptors are clustered. We also hypothesize that the adaptor protein hopping mechanism can cause concurrent or sequential activation of multiple signaling pathways, thus leading to crosstalk between diverse biological functions.
doi:10.3233/FI-2012-720
PMCID: PMC3865981  PMID: 24357890
5.  Interdisciplinary dialogue for education, collaboration, and innovation: Intelligent Biology and Medicine in and beyond 2013 
BMC Genomics  2013;14(Suppl 8):S1.
The 2013 International Conference on Intelligent Biology and Medicine (ICIBM 2013) was held on August 11-13, 2013 in Nashville, Tennessee, USA. The conference included six scientific sessions, two tutorial sessions, one workshop, two poster sessions, and four keynote presentations that covered cutting-edge research topics in bioinformatics, systems biology, computational medicine, and intelligent computing. Here, we present a summary of the conference and an editorial report of the supplements to BMC Genomics and BMC Systems Biology that include 19 research papers selected from ICIBM 2013.
doi:10.1186/1471-2164-14-S8-S1
PMCID: PMC4042234  PMID: 24564388
6.  RNA Type III Secretion Signals That Require Hfq 
Journal of Bacteriology  2013;195(10):2119-2125.
Salmonella virulence is largely mediated by two type III secretion systems (T3SS) that deliver effector proteins from the bacterium to a host cell; however, the secretion signal is poorly defined. Effector N termini are thought to contain the signal, but they lack homology, possess no identifiable motif, and adopt intrinsically disordered structures. Alternative studies suggest that RNA-encoded signals may also be recognized and that they can be located in the 5′ untranslated leader sequence. We began our study by establishing the minimum sequence required for reporter translocation. Untranslated leader sequences predicted from 42 different Salmonella effector proteins were fused to the adenylate cyclase reporter (CyaA′), and each of them was tested for protein injection into J774 macrophages. RNA sequences derived from five effectors, gtgA, cigR, gogB, sseL, and steD, were sufficient for CyaA′ translocation into host cells. To determine the mechanism of signal recognition, we identified proteins that bound specifically to the gtgA RNA. One of the unique proteins identified was Hfq. Hfq had no effect upon the translocation of full-length CigR and SteD, but injection of intact GtgA, GogB, and SseL was abolished in an hfq mutant, confirming the importance of Hfq. Our results demonstrated that the Salmonella pathogenicity island 2 (SPI-2) T3SS assembled into a functional apparatus independently of Hfq. Since particular effectors required Hfq for translocation, Hfq-RNA complexes may participate in signal recognition.
doi:10.1128/JB.00024-13
PMCID: PMC3650527  PMID: 23396917
7.  A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification 
Disease markers  2013;35(5):513-523.
Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.
doi:10.1155/2013/613529
PMCID: PMC3809975  PMID: 24223463
8.  A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses 
PLoS ONE  2013;8(7):e69374.
Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.
doi:10.1371/journal.pone.0069374
PMCID: PMC3723910  PMID: 23935999
9.  Proteome and Computational Analyses Reveal New Insights into the Mechanisms of Hepatitis C Virus Mediated Liver Disease Post-Transplantation 
Hepatology (Baltimore, Md.)  2012;56(1):28-38.
Liver transplant tissues offer the unique opportunity to model the longitudinal protein abundance changes occurring during hepatitis C virus (HCV)-associated liver disease progression in vivo. In this study, our goal was to identify molecular signatures, and potential key regulatory proteins, representative of the processes influencing early progression to fibrosis. We performed global protein profiling analyses on 24 liver biopsy specimens obtained from 15 HCV+ liver transplant recipients at 6 and/or 12 months post-transplantation. Differentially regulated proteins associated with early progression to fibrosis were identified by analysis of the area under the receiver operating characteristic curve (AUC). Analysis of serum metabolites was performed on samples obtained from an independent cohort of 60 HCV+ liver transplant patients. Computational modeling approaches were applied to identify potential key regulatory proteins of liver fibrogenesis. Among 4,324 proteins identified, 250 exhibited significant differential regulation in patients with rapidly progressive fibrosis. Patients with rapid fibrosis progression exhibited enrichment in differentially regulated proteins associated with various immune, hepatoprotective, and fibrogenic processes. The observed increase in pro-inflammatory activity and impairment in anti-oxidant defenses suggests that patients who develop significant liver injury experience elevated oxidative stresses. This was supported by an independent study demonstrating the altered abundance of oxidative stress associated serum metabolites in patients who develop severe liver injury. Computational modeling approaches further highlight a potentially important link between HCV-associated oxidative stress and epigenetic regulatory mechanisms impacting on liver fibrogenesis. In conclusion, our proteome and metabolome analyses provide new insights into the role for increased oxidative stress in the rapid fibrosis progression observed in HCV+ liver transplant recipients. These findings may prove useful in prognostic applications for predicting early progression to fibrosis.
doi:10.1002/hep.25649
PMCID: PMC3387320  PMID: 22331615
liver biopsy; systems biology; protein bottleneck
10.  Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data 
Introduction
The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches.
Areas covered
In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches.
Expert opinion
Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.
doi:10.1517/17530059.2012.718329
PMCID: PMC3548234  PMID: 23335946
biomarkers; molecular signatures; classification; data integration; expert knowledge
11.  Modeling Dynamic Regulatory Processes in Stroke 
PLoS Computational Biology  2012;8(10):e1002722.
The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.
Author Summary
Computational modeling aims to use mathematical and algorithmic principles to link components of biological systems to predict system behavior. In the past such models have described a small set of carefully studied molecular interactions (proteins in signal transduction pathways) or larger abstract components (cell types or functional processes in the immune system). In this study we use data from global transcriptional analysis of the processes of neuroprotection in a mouse model of stroke to generate functional modules, groups of genes that coherently act to accomplish functions. We then derive equations relating the expression of these modules to one another, treating these individual equations as a closed system, and demonstrate that the model can be used to simulate the gene expression of the system over time. Our work is novel in describing the use of global transcriptomic data to develop dynamic models of expression in an animal model. We believe that the models developed will aid in understanding the complex dynamics of neuroprotection and provide ways to predict outcomes in terms of neuroprotection or injury. This approach will be broadly applicable to other problems and provides an approach to building dynamic models from the bottom up.
doi:10.1371/journal.pcbi.1002722
PMCID: PMC3469412  PMID: 23071432
12.  Suppressed Expression of T-Box Transcription Factors Is Involved in Senescence in Chronic Obstructive Pulmonary Disease 
PLoS Computational Biology  2012;8(7):e1002597.
Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved. We conducted an exploratory study on COPD etiology to identify the key molecular participants. We used information-theoretic algorithms including Context Likelihood of Relatedness (CLR), Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Inferelator. We captured direct functional associations among genes, given a compendium of gene expression profiles of human lung epithelial cells. A set of genes differentially expressed in COPD, as reported in a previous study were superposed with the resulting transcriptional regulatory networks. After factoring in the properties of the networks, an established COPD susceptibility locus and domain-domain interactions involving protein products of genes in the generated networks, several molecular candidates were predicted to be involved in the etiology of COPD. These include COL4A3, CFLAR, GULP1, PDCD1, CASP10, PAX3, BOK, HSPD1, PITX2, and PML. Furthermore, T-box (TBX) genes and cyclin-dependent kinase inhibitor 2A (CDKN2A), which are in a direct transcriptional regulatory relationship, emerged as preeminent participants in the etiology of COPD by means of senescence. Contrary to observations in neoplasms, our study reveals that the expression of genes and proteins in the lung samples from patients with COPD indicate an increased tendency towards cellular senescence. The expression of the anti-senescence mediators TBX transcription factors, chromatin modifiers histone deacetylases, and sirtuins was suppressed; while the expression of TBX-regulated cellular senescence markers such as CDKN2A, CDKN1A, and CAV1 was elevated in the peripheral lung tissue samples from patients with COPD. The critical balance between senescence and anti-senescence factors is disrupted towards senescence in COPD lungs.
Author Summary
Chronic obstructive pulmonary disease or COPD is among the most lethal of respiratory diseases. While this disease has been well characterized, more studies are needed to learn the interaction of macromolecules involved in the progression towards illness. We explored possible interactions involved in the disease process using a compendium of gene expression data from frontline cells of the respiratory airways of the lung. The gene expression data were generated under a variety of experimental conditions. Application of computational schemes, which robustly detect enduring patterns, among sections of the genes represented across the varying experimental perturbations, revealed important regulatory relationships. When gene expression data from lungs of patients with COPD were factored into these networks of regulatory relationships, certain highly connected nodes (hubs) representing differentially expressed genes emerged. Notably included are members of the T-box (TBX) family of genes and CDKN2A, which regulate cellular aging. These findings were confirmed in studies using lung samples from COPD patients. Novel genes linked to TBX and CDKN2A include COL4A3, CFLAR, GULP1, PDCD1, CASP10, PAX3, BOK, HSPD1, PITX2, and PML, which were thus predicted to be involved in the disease process. The balance between senescence and anti-senescence factors is disrupted towards senescence in COPD lungs.
doi:10.1371/journal.pcbi.1002597
PMCID: PMC3400575  PMID: 22829758
13.  Identification and Validation of Ifit1 as an Important Innate Immune Bottleneck 
PLoS ONE  2012;7(6):e36465.
The innate immune system plays important roles in a number of disparate processes. Foremost, innate immunity is a first responder to invasion by pathogens and triggers early defensive responses and recruits the adaptive immune system. The innate immune system also responds to endogenous damage signals that arise from tissue injury. Recently it has been found that innate immunity plays an important role in neuroprotection against ischemic stroke through the activation of the primary innate immune receptors, Toll-like receptors (TLRs). Using several large-scale transcriptomic data sets from mouse and mouse macrophage studies we identified targets predicted to be important in controlling innate immune processes initiated by TLR activation. Targets were identified as genes with high betweenness centrality, so-called bottlenecks, in networks inferred from statistical associations between gene expression patterns. A small set of putative bottlenecks were identified in each of the data sets investigated including interferon-stimulated genes (Ifit1, Ifi47, Tgtp and Oasl2) as well as genes uncharacterized in immune responses (Axud1 and Ppp1r15a). We further validated one of these targets, Ifit1, in mouse macrophages by showing that silencing it suppresses induction of predicted downstream genes by lipopolysaccharide (LPS)-mediated TLR4 activation through an unknown direct or indirect mechanism. Our study demonstrates the utility of network analysis for identification of interesting targets related to innate immune function, and highlights that Ifit1 can exert a positive regulatory effect on downstream genes.
doi:10.1371/journal.pone.0036465
PMCID: PMC3380000  PMID: 22745654
14.  Systems Virology Identifies a Mitochondrial Fatty Acid Oxidation Enzyme, Dodecenoyl Coenzyme A Delta Isomerase, Required for Hepatitis C Virus Replication and Likely Pathogenesis▿ † 
Journal of Virology  2011;85(22):11646-11654.
We previously employed systems biology approaches to identify the mitochondrial fatty acid oxidation enzyme dodecenoyl coenzyme A delta isomerase (DCI) as a bottleneck protein controlling host metabolic reprogramming during hepatitis C virus (HCV) infection. Here we present the results of studies confirming the importance of DCI to HCV pathogenesis. Computational models incorporating proteomic data from HCV patient liver biopsy specimens recapitulated our original predictions regarding DCI and link HCV-associated alterations in cellular metabolism and liver disease progression. HCV growth and RNA replication in hepatoma cell lines stably expressing DCI-targeting short hairpin RNA (shRNA) were abrogated, indicating that DCI is required for productive infection. Pharmacologic inhibition of fatty acid oxidation also blocked HCV replication. Production of infectious HCV was restored by overexpression of an shRNA-resistant DCI allele. These findings demonstrate the utility of systems biology approaches to gain novel insight into the biology of HCV infection and identify novel, translationally relevant therapeutic targets.
doi:10.1128/JVI.05605-11
PMCID: PMC3209311  PMID: 21917952
15.  Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis 
BMC Systems Biology  2012;6:28.
Background
High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV) infection.
Results
We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture.
Conclusions
The results presented show that the network topology of protein co-abundance networks can be used to identify proteins important for viral replication. These proteins represent targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat HCV infection.
doi:10.1186/1752-0509-6-28
PMCID: PMC3383540  PMID: 22546282
16.  A multi-pronged search for a common structural motif in the secretion signal of Salmonella enterica serovar Typhimurium type III effector proteins 
Molecular Biosystems  2010;6(12):2448-2458.
Many pathogenic Gram-negative bacteria use a type III secretion system (T3SS) to deliver effector proteins into the host cell where they reprogram host defenses and facilitate pathogenesis. The first 20–30 N-terminal residues usually contain the ‘secretion signal’ that targets effector proteins for translocation, however, a consensus sequence motif has never been discerned. Recent machine-learning approaches, such as support vector machine (SVM)-based Identification and Evaluation of Virulence Effectors (SIEVE), have improved the ability to identify effector proteins from genomics sequence information. While these methods all suggest that the T3SS secretion signal has a characteristic amino acid composition bias, it is still unclear if the amino acid pattern is important and if there are any unifying structural properties that direct recognition. To address these issues a peptide corresponding to the secretion signal for Salmonella enterica serovar Typhimurium effector SseJ was synthesized (residues 1–30, SseJ) along with scrambled peptides of the same amino acid composition that produced high (SseJ-H) and low (SseJ-L) SIEVE scores. The secretion properties of these three peptides were tested using a secretion signal–CyaA fusion assay and their structural properties probed using circular dichroism, nuclear magnetic resonance, and ion mobility spectrometry–mass spectrometry. The secretion predictions from SIEVE matched signal–CyaA fusion experimental results with J774 macrophages suggesting that the SseJ secretion signal has some sequence order dependence. The structural studies showed that the SseJ, SseJ-H, and SseJ-L peptides were intrinsically disordered in aqueous solution with a small predisposition to adopt nascent helical structure only in the presence of structure stabilizing agents such as 1,1,1,3,3,3-hexafluoroisopropanol. Intrinsic disorder may be a universal feature of effector secretion signals as similar conclusions were reached following structural characterization of peptides corresponding to the N-terminal regions of the S. Typhimurium effectors SptP, SopD-2, GtgE, and the Yersinia pestis effector YopH.
doi:10.1039/c0mb00097c
PMCID: PMC3282560  PMID: 20877914
17.  Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems 
BMC Systems Biology  2011;5:190.
Background
Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades.
Results
In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms.
Conclusions
This is the first demonstration of a global regulatory network modeling conserved host response between in vitro and in vivo models.
doi:10.1186/1752-0509-5-190
PMCID: PMC3229612  PMID: 22074594
systems biology; influenza infection; host response; network inference; comparative transcriptomics
18.  Discovery of Novel Secreted Virulence Factors from Salmonella enterica Serovar Typhimurium by Proteomic Analysis of Culture Supernatants ▿ #  
Infection and Immunity  2010;79(1):33-43.
Salmonella enterica serovar Typhimurium is a leading cause of acute gastroenteritis throughout the world. This pathogen has two type III secretion systems (TTSS) encoded in Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2) that deliver virulence factors (effectors) to the host cell cytoplasm and are required for virulence. While many effectors have been identified and at least partially characterized, the full repertoire of effectors has not been catalogued. In this proteomic study, we identified effector proteins secreted into defined minimal medium designed to induce expression of the SPI-2 TTSS and its effectors. We compared the secretomes of the parent strain to those of strains missing essential (ssaK::cat) or regulatory (ΔssaL) components of the SPI-2 TTSS. We identified 20 known SPI-2 effectors. Excluding the translocon components SseBCD, all SPI-2 effectors were biased for identification in the ΔssaL mutant, substantiating the regulatory role of SsaL in TTS. To identify novel effector proteins, we coupled our secretome data with a machine learning algorithm (SIEVE, SVM-based identification and evaluation of virulence effectors) and selected 12 candidate proteins for further characterization. Using CyaA′ reporter fusions, we identified six novel type III effectors and two additional proteins that were secreted into J774 macrophages independently of a TTSS. To assess their roles in virulence, we constructed nonpolar deletions and performed a competitive index analysis from intraperitoneally infected 129/SvJ mice. Six mutants were significantly attenuated for spleen colonization. Our results also suggest that non-type III secretion mechanisms are required for full Salmonella virulence.
doi:10.1128/IAI.00771-10
PMCID: PMC3019877  PMID: 20974834
19.  Computational Prediction of Type III and IV Secreted Effectors in Gram-Negative Bacteria ▿  
Infection and Immunity  2010;79(1):23-32.
In this review, we provide an overview of the methods employed in four recent studies that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. We present the results of these studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the Web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVE server (http://www.sysbep.org/sieve). Finally, we assess the accuracies of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general, arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.
doi:10.1128/IAI.00537-10
PMCID: PMC3019878  PMID: 20974833
20.  Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella 
BMC Systems Biology  2011;5:100.
Background
Systemic bacterial infections are highly regulated and complex processes that are orchestrated by numerous virulence factors. Genes that are coordinately controlled by the set of regulators required for systemic infection are potentially required for pathogenicity.
Results
In this study we present a systems biology approach in which sample-matched multi-omic measurements of fourteen virulence-essential regulator mutants were coupled with computational network analysis to efficiently identify Salmonella virulence factors. Immunoblot experiments verified network-predicted virulence factors and a subset was determined to be secreted into the host cytoplasm, suggesting that they are virulence factors directly interacting with host cellular components. Two of these, SrfN and PagK2, were required for full mouse virulence and were shown to be translocated independent of either of the type III secretion systems in Salmonella or the type III injectisome-related flagellar mechanism.
Conclusions
Integrating multi-omic datasets from Salmonella mutants lacking virulence regulators not only identified novel virulence factors but also defined a new class of translocated effectors involved in pathogenesis. The success of this strategy at discovery of known and novel virulence factors suggests that the approach may have applicability for other bacterial pathogens.
doi:10.1186/1752-0509-5-100
PMCID: PMC3213010  PMID: 21711513
21.  Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella 
Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.
doi:10.3389/fmicb.2011.00121
PMCID: PMC3108385  PMID: 21687430
Salmonella; virulence; regulatory network; regulators; transcriptomics; proteomics; computational modeling
23.  DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS 
Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach to expand the set of regulators considered and show that addition of topological bottlenecks improves the performance of the predictive model. Finally, we explore how improvements in definition of functional modules may be achieved through an integration of inferred network relationships and functional relationships defined using Gene Ontology similarity. We show that appropriate integration of these two types of relationships can result in models with improved performance.
PMCID: PMC3052927  PMID: 21121059
24.  Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation 
PLoS ONE  2011;6(2):e14673.
We have investigated macrophage activation using computational analyses of a compendium of transcriptomic data covering responses to agonists of the TLR pathway, Salmonella infection, and manufactured amorphous silica nanoparticle exposure. We inferred regulatory relationship networks using this compendium and discovered that genes with high betweenness centrality, so-called bottlenecks, code for proteins targeted by pathogens. Furthermore, combining a novel set of bioinformatics tools, topological analysis with analysis of differentially expressed genes under the different stimuli, we identified a conserved core response module that is differentially expressed in response to all studied conditions. This module occupies a highly central position in the inferred network and is also enriched in genes preferentially targeted by pathogens. The module includes cytokines, interferon induced genes such as Ifit1 and 2, effectors of inflammation, Cox1 and Oas1 and Oasl2, and transcription factors including AP1, Egr1 and 2 and Mafb. Predictive modeling using a reverse-engineering approach reveals dynamic differences between the responses to each stimulus and predicts the regulatory influences directing this module. We speculate that this module may be an early checkpoint for progression to apoptosis and/or inflammation during macrophage activation.
doi:10.1371/journal.pone.0014673
PMCID: PMC3038849  PMID: 21339814
25.  A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm 
mBio  2011;2(1):e00325-10.
The twentieth century was marked by extraordinary advances in our understanding of microbes and infectious disease, but pandemics remain, food and waterborne illnesses are frequent, multidrug-resistant microbes are on the rise, and the needed drugs and vaccines have not been developed. The scientific approaches of the past—including the intense focus on individual genes and proteins typical of molecular biology—have not been sufficient to address these challenges. The first decade of the twenty-first century has seen remarkable innovations in technology and computational methods. These new tools provide nearly comprehensive views of complex biological systems and can provide a correspondingly deeper understanding of pathogen-host interactions. To take full advantage of these innovations, the National Institute of Allergy and Infectious Diseases recently initiated the Systems Biology Program for Infectious Disease Research. As participants of the Systems Biology Program, we think that the time is at hand to redefine the pathogen-host research paradigm.
doi:10.1128/mBio.00325-10
PMCID: PMC3034460  PMID: 21285433

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