Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism.

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.

Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.

Multidimensional Clinical Surveillance of Pseudomonas aeruginosa Reveals Complex Relationships between Isolate Source, Morphology, and Antimicrobial Resistance.

Antimicrobial susceptibility in Pseudomonas aeruginosa is dependent on a complex combination of host and pathogen-specific factors. Through the profiling of 971 clinical P. aeruginosa isolates from 590 patients and collection of paired patient metadata, we show that antimicrobial resistance is associated with not only patient-centric factors (e.g., cystic fibrosis and antipseudomonal prescription history) but also microbe-specific phenotypes (e.g., mucoid colony morphology). Additionally, isolates from different sources (e.g., respiratory tract, urinary tract) displayed rates of antimicrobial resistance that were correlated with source-specific antimicrobial prescription strategies. Furthermore, isolates from the same patient often displayed a high degree of heterogeneity, highlighting a key challenge facing personalized treatment of infectious diseases. Our findings support novel relationships between isolate and patient-level data sets, providing a potential guide for future antimicrobial treatment strategies. IMPORTANCE P. aeruginosa is a leading cause of nosocomial infection and infection in patients with cystic fibrosis. While P. aeruginosa infection and treatment can be complicated by a variety of antimicrobial resistance and virulence mechanisms, pathogen virulence is rarely recorded in a clinical setting. In this study, we discovered novel relationships between antimicrobial resistance, virulence-linked morphologies, and isolate source in a large and variable collection of clinical P. aeruginosa isolates. Our work motivates the clinical surveillance of virulence-linked P. aeruginosa morphologies as well as the tracking of source-specific antimicrobial prescription and resistance patterns.

Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus.

While bacterial metabolism is known to impact antibiotic efficacy and virulence, the metabolic capacities of individual microbes in cystic fibrosis lung infections are difficult to disentangle from sputum samples. Here, we show that untargeted metabolomic profiling of supernatants of multiple strains of Pseudomonas aeruginosa and Staphylococcus aureus grown in monoculture in synthetic cystic fibrosis media (SCFM) reveals distinct species-specific metabolic signatures despite intraspecies metabolic variability. We identify a set of 15 metabolites that were significantly consumed by both P. aeruginosa and S. aureus, suggesting that nutrient competition has the potential to impact community dynamics even in the absence of other pathogen-pathogen interactions. Finally, metabolites that were uniquely produced by one species or the other were identified. Specifically, the virulence factor precursor anthranilic acid, as well as the quinoline 2,4-quinolinediol (DHQ), were robustly produced across all tested strains of P. aeruginosa. Through the direct comparison of the extracellular metabolism of P. aeruginosa and S. aureus in a physiologically relevant environment, this work provides insight toward the potential for metabolic interactions in vivo and supports the development of species-specific diagnostic markers of infection. IMPORTANCE Interactions between P. aeruginosa and S. aureus can impact pathogenicity and antimicrobial efficacy. In this study, we aim to better understand the potential for metabolic interactions between P. aeruginosa and S. aureus in an environment resembling the cystic fibrosis lung. We find that S. aureus and P. aeruginosa consume many of the same nutrients, suggesting that metabolic competition may play an important role in community dynamics during coinfection. We further identify metabolites uniquely produced by either organism with the potential to be developed into species-specific biomarkers of infection in the cystic fibrosis lung.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions.

Interactions between microbes are central to the dynamics of microbial communities. Understanding these interactions is essential for the characterization of communities, yet challenging to accomplish in practice. There are limited available tools for characterizing diffusion-mediated, contact-independent microbial interactions. A practical and widely implemented technique in such characterization involves the simultaneous co-culture of distinct bacterial species and subsequent analysis of relative abundance in the total population. However, distinguishing between species can be logistically challenging. In this paper, we present a low-cost, vertical membrane, co-culture plate to quantify contact-independent interactions between distinct bacterial populations in co-culture via real-time optical density measurements. These measurements can be used to facilitate the analysis of the interaction between microbes that are physically separated by a semipermeable membrane yet able to exchange diffusible molecules. We show that diffusion across the membrane occurs at a sufficient rate to enable effective interaction between physically separate cultures. Two bacterial species commonly found in the cystic fibrotic lung, Pseudomonas aeruginosa and Burkholderia cenocepacia, were co-cultured to demonstrate how this plate may be implemented to study microbial interactions. We have demonstrated that this novel co-culture device is able to reliably generate real-time measurements of optical density data that can be used to characterize interactions between microbial species.

Increased Urinary Trimethylamine N-Oxide Following Cryptosporidium Infection and Protein Malnutrition Independent of Microbiome Effects.

Cryptosporidium infections have been associated with growth stunting, even in the absence of diarrhea. Having previously detailed the effects of protein deficiency on both microbiome and metabolome in this model, we now describe the specific gut microbial and biochemical effects of Cryptosporidium infection. Protein-deficient mice were infected with Cryptosporidium parvum oocysts for 6-13 days and compared with uninfected controls. Following infection, there was an increase in the urinary excretion of choline- and amino-acid-derived metabolites. Conversely, infection reduced the excretion of the microbial-host cometabolite (3-hydroxyphenyl)propionate-sulfate and disrupted metabolites involved in the tricarboxylic acid (TCA) cycle. Correlation analysis of microbial and biochemical profiles resulted in associations between various microbiota members and TCA cycle metabolites, as well as some microbial-specific degradation products. However, no correlation was observed between the majority of the infection-associated metabolites and the fecal bacteria, suggesting that these biochemical perturbations are independent of concurrent changes in the relative abundance of members of the microbiota. We conclude that cryptosporidial infection in protein-deficient mice can mimic some metabolic changes seen in malnourished children and may help elucidate our understanding of long-term metabolic consequences of early childhood enteric infections.

Growth-altering microbial interactions are responsive to chemical context.

Microbial interactions are ubiquitous in nature, and are equally as relevant to human wellbeing as the identities of the interacting microbes. However, microbial interactions are difficult to measure and characterize. Furthermore, there is growing evidence that they are not fixed, but dependent on environmental context. We present a novel workflow for inferring microbial interactions that integrates semi-automated image analysis with a colony stamping mechanism, with the overall effect of improving throughput and reproducibility of colony interaction assays. We apply our approach to infer interactions among bacterial species associated with the normal lung microbiome, and how those interactions are altered by the presence of benzo[a]pyrene, a carcinogenic compound found in cigarettes. We found that the presence of this single compound changed the interaction network, demonstrating that microbial interactions are indeed dynamic and responsive to local chemical context.

Protein- and zinc-deficient diets modulate the murine microbiome and metabolic phenotype.

BACKGROUND: Environmental enteropathy, which is linked to undernutrition and chronic infections, affects the physical and mental growth of children in developing areas worldwide. Key to understanding how these factors combine to shape developmental outcomes is to first understand the effects of nutritional deficiencies on the mammalian system including the effect on the gut microbiota. OBJECTIVE: We dissected the nutritional components of environmental enteropathy by analyzing the specific metabolic and gut-microbiota changes that occur in weaned-mouse models of zinc or protein deficiency compared with well-nourished controls. DESIGN: With the use of a 1H nuclear magnetic resonance spectroscopy-based metabolic profiling approach with matching 16S microbiota analyses, the metabolic consequences and specific effects on the fecal microbiota of protein and zinc deficiency were probed independently in a murine model. RESULTS: We showed considerable shifts within the intestinal microbiota 14-24 d postweaning in mice that were maintained on a normal diet (including increases in Proteobacteria and striking decreases in Bacterioidetes). Although the zinc-deficient microbiota were comparable to the age-matched, well-nourished profile, the protein-restricted microbiota remained closer in composition to the weaned enterotype with retention of Bacteroidetes. Striking increases in Verrucomicrobia (predominantly Akkermansia muciniphila) were observed in both well-nourished and protein-deficient mice 14 d postweaning. We showed that protein malnutrition impaired growth and had major metabolic consequences (much more than with zinc deficiency) that included altered energy, polyamine, and purine and pyrimidine metabolism. Consistent with major changes in the gut microbiota, reductions in microbial proteolysis and increases in microbial dietary choline processing were observed. CONCLUSIONS: These findings are consistent with metabolic alterations that we previously observed in malnourished children. The results show that we can model the metabolic consequences of malnutrition in the mouse to help dissect relevant pathways involved in the effects of undernutrition and their contribution to environmental enteric dysfunction.

Genotypic and phenotypic analyses of a Pseudomonas aeruginosa chronic bronchiectasis isolate reveal differences from cystic fibrosis and laboratory strains.

BACKGROUND: Pseudomonas aeruginosa is an environmentally ubiquitous Gram-negative bacterium and important opportunistic human pathogen, causing severe chronic respiratory infections in patients with underlying conditions such as cystic fibrosis (CF) or bronchiectasis. In order to identify mechanisms responsible for adaptation during bronchiectasis infections, a bronchiectasis isolate, PAHM4, was phenotypically and genotypically characterized. RESULTS: This strain displays phenotypes that have been associated with chronic respiratory infections in CF including alginate over-production, rough lipopolysaccharide, quorum-sensing deficiency, loss of motility, decreased protease secretion, and hypermutation. Hypermutation is a key adaptation of this bacterium during the course of chronic respiratory infections and analysis indicates that PAHM4 encodes a mutated mutS gene responsible for a ~1,000-fold increase in mutation rate compared to wild-type laboratory strain P. aeruginosa PAO1. Antibiotic resistance profiles and sequence data indicate that this strain acquired numerous mutations associated with increased resistance levels to ß-lactams, aminoglycosides, and fluoroquinolones when compared to PAO1. Sequencing of PAHM4 revealed a 6.38 Mbp genome, 5.9 % of which were unrecognized in previously reported P. aeruginosa genome sequences. Transcriptome analysis suggests a general down-regulation of virulence factors, while metabolism of amino acids and lipids is up-regulated when compared to PAO1 and metabolic modeling identified further potential differences between PAO1 and PAHM4. CONCLUSIONS: This work provides insights into the potential differential adaptation of this bacterium to the lung of patients with bronchiectasis compared to other clinical settings such as cystic fibrosis, findings that should aid the development of disease-appropriate treatment strategies for P. aeruginosa infections.

High temporal resolution of glucosyltransferase dependent and independent effects of Clostridium difficile toxins across multiple cell types.

BACKGROUND: Clostridium difficile toxins A and B (TcdA and TcdB), considered to be essential for C. difficile infection, affect the morphology of several cell types with different potencies and timing. However, morphological changes over various time scales are poorly characterized. The toxins' glucosyltransferase domains are critical to their deleterious effects, and cell responses to glucosyltransferase-independent activities are incompletely understood. By tracking morphological changes of multiple cell types to C. difficile toxins with high temporal resolution, cellular responses to TcdA, TcdB, and a glucosyltransferase-deficient TcdB (gdTcdB) are elucidated. RESULTS: Human umbilical vein endothelial cells, J774 macrophage-like cells, and four epithelial cell lines (HCT8, T84, CHO, and immortalized mouse cecal epithelial cells) were treated with TcdA, TcdB, gdTcdB. Impedance across cell cultures was measured to track changes in cell morphology. Metrics from impedance data, developed to quantify rapid and long-lasting responses, produced standard curves with wide dynamic ranges that defined cell line sensitivities. Except for T84 cells, all cell lines were most sensitive to TcdB. J774 macrophages stretched and increased in size in response to TcdA and TcdB but not gdTcdB. High concentrations of TcdB and gdTcdB (>10 ng/ml) greatly reduced macrophage viability. In HCT8 cells, gdTcdB did not induce a rapid cytopathic effect, yet it delayed TcdA and TcdB's rapid effects. gdTcdB did not clearly delay TcdA or TcdB's toxin-induced effects on macrophages. CONCLUSIONS: Epithelial and endothelial cells have similar responses to toxins yet differ in timing and degree. Relative potencies of TcdA and TcdB in mouse epithelial cells in vitro do not correlate with potencies in vivo. TcdB requires glucosyltransferase activity to cause macrophages to spread, but cell death from high TcdB concentrations is glucosyltransferase-independent. Competition experiments with gdTcdB in epithelial cells confirm common TcdA and TcdB mechanisms, yet different responses of macrophages to TcdA and TcdB suggest different, additional mechanisms or targets in these cells. This first-time, precise quantification of the response of multiple cell lines to TcdA and TcdB provides a comparative framework for delineating the roles of different cell types and toxin-host interactions.

A thirteen-gene expression signature predicts survival of patients with pancreatic cancer and identifies new genes of interest.

BACKGROUND: Currently, prognostication for pancreatic ductal adenocarcinoma (PDAC) is based upon a coarse clinical staging system. Thus, more accurate prognostic tests are needed for PDAC patients to aid treatment decisions. METHODS AND FINDINGS: Affymetrix gene expression profiling was carried out on 15 human PDAC tumors and from the data we identified a 13-gene expression signature (risk score) that correlated with patient survival. The gene expression risk score was then independently validated using published gene expression data and survival data for an additional 101 patients with pancreatic cancer. Patients with high-risk scores had significantly higher risk of death compared to patients with low-risk scores (HR 2.27, p = 0.002). When the 13-gene score was combined with lymph node status the risk-score further discriminated the length of patient survival time (p<0.001). Patients with a high-risk score had poor survival independent of nodal status; however, nodal status increased predictability for survival in patients with a low-risk gene signature score (low-risk N1 vs. low-risk N0: HR = 2.0, p = 0.002). While AJCC stage correlated with patient survival (p = 0.03), the 13-gene score was superior at predicting survival. Of the 13 genes comprising the predictive model, four have been shown to be important in PDAC, six are unreported in PDAC but important in other cancers, and three are unreported in any cancer. CONCLUSIONS: We identified a 13-gene expression signature that predicts survival of PDAC patients and could prove useful for making treatment decisions. This risk score should be evaluated prospectively in clinical trials for prognostication and for predicting response to chemotherapy. Investigation of new genes identified in our model may lead to novel therapeutic targets.

Comparative metabolic systems analysis of pathogenic Burkholderia.

Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens.

Clinical, molecular and genetic validation of a murine orthotopic xenograft model of pancreatic adenocarcinoma using fresh human specimens.

BACKGROUND: Relevant preclinical models that recapitulate the key features of human pancreatic ductal adenocarcinoma (PDAC) are needed in order to provide biologically tractable models to probe disease progression and therapeutic responses and ultimately improve patient outcomes for this disease. Here, we describe the establishment and clinical, pathological, molecular and genetic validation of a murine, orthotopic xenograft model of PDAC. METHODS: Human PDACs were resected and orthotopically implanted and propagated in immunocompromised mice. Patient survival was correlated with xenograft growth and metastatic rate in mice. Human and mouse tumor pathology were compared. Tumors were analyzed for genetic mutations, gene expression, receptor tyrosine kinase activation, and cytokine expression. RESULTS: Fifteen human PDACs were propagated orthotopically in mice. Xenograft-bearing mice developed peritoneal and liver metastases. Time to tumor growth and metastatic efficiency in mice each correlated with patient survival. Tumor architecture, nuclear grade and stromal content were similar in patient and xenografted tumors. Propagated tumors closely exhibited the genetic and molecular features known to characterize pancreatic cancer (e.g. high rate of KRAS, P53, SMAD4 mutation and EGFR activation). The correlation coefficient of gene expression between patient tumors and xenografts propagated through multiple generations was 93 to 99%. Analysis of gene expression demonstrated distinct differences between xenografts from fresh patient tumors versus commercially available PDAC cell lines. CONCLUSIONS: The orthotopic xenograft model derived from fresh human PDACs closely recapitulates the clinical, pathologic, genetic and molecular aspects of human disease. This model has resulted in the identification of rational therapeutic strategies to be tested in clinical trials and will permit additional therapeutic approaches and identification of biomarkers of response to therapy.

MicroRNAs induced in melanoma treated with combination targeted therapy of Temsirolimus and Bevacizumab.

BACKGROUND: Targeted therapies directed at commonly overexpressed pathways in melanoma have clinical activity in numerous trials. Little is known about how these therapies influence microRNA (miRNA) expression, particularly with combination regimens. Knowledge of miRNAs altered with treatment may contribute to understanding mechanisms of therapeutic effects, as well as mechanisms of tumor escape from therapy. We analyzed miRNA expression in metastatic melanoma tissue samples treated with a novel combination regimen of Temsirolimus and Bevacizumab. Given the preliminary clinical activity observed with this combination regimen, we hypothesized that we would see significant changes in miRNA expression with combination treatment. METHODS: Using microarray analysis we analyzed miRNA expression levels in melanoma samples from a Cancer Therapy Evaluation Program-sponsored phase II trial of combination Temsirolimus and Bevacizumab in advanced melanoma, which elicited clinical benefit in a subset of patients. Pre-treatment and post-treatment miRNA levels were compared using paired t-tests between sample groups (patients), using a p-value < 0.01 for significance. RESULTS: microRNA expression remained unchanged with Temsirolimus alone; however, expression of 15 microRNAs was significantly upregulated (1.4 to 2.5-fold) with combination treatment, compared to pre-treatment levels. Interestingly, twelve of these fifteen miRNAs possess tumor suppressor capabilities. We identified 15 putative oncogenes as potential targets of the 12 tumor suppressor miRNAs, based on published experimental evidence. For 15 of 25 miRNA-target mRNA pairings, changes in gene expression from pre-treatment to post-combination treatment samples were inversely correlated with changes in miRNA expression, supporting a functional effect of those miRNA changes. Clustering analyses based on selected miRNAs suggest preliminary signatures characteristic of clinical response to combination treatment and of tumor BRAF mutational status. CONCLUSIONS: To our knowledge, this is the first study analyzing miRNA expression in pre-treatment and post-treatment human metastatic melanoma tissue samples. This preliminary investigation suggests miRNAs that may be involved in the mechanism of action of combination Temsirolimus and Bevacizumab in metastatic melanoma, possibly through inhibition of oncogenic pathways, and provides the preliminary basis for further functional studies of these miRNAs.

Matrix rigidity regulates cancer cell growth by modulating cellular metabolism and protein synthesis.

BACKGROUND: Tumor cells in vivo encounter diverse types of microenvironments both at the site of the primary tumor and at sites of distant metastases. Understanding how the various mechanical properties of these microenvironments affect the biology of tumor cells during disease progression is critical in identifying molecular targets for cancer therapy. METHODOLOGY/PRINCIPAL FINDINGS: This study uses flexible polyacrylamide gels as substrates for cell growth in conjunction with a novel proteomic approach to identify the properties of rigidity-dependent cancer cell lines that contribute to their differential growth on soft and rigid substrates. Compared to cells growing on more rigid/stiff substrates (>10,000 Pa), cells on soft substrates (150-300 Pa) exhibited a longer cell cycle, due predominantly to an extension of the G1 phase of the cell cycle, and were metabolically less active, showing decreased levels of intracellular ATP and a marked reduction in protein synthesis. Using stable isotope labeling of amino acids in culture (SILAC) and mass spectrometry, we measured the rates of protein synthesis of over 1200 cellular proteins under growth conditions on soft and rigid/stiff substrates. We identified cellular proteins whose syntheses were either preferentially inhibited or preserved on soft matrices. The former category included proteins that regulate cytoskeletal structures (e.g., tubulins) and glycolysis (e.g., phosphofructokinase-1), whereas the latter category included proteins that regulate key metabolic pathways required for survival, e.g., nicotinamide phosphoribosyltransferase, a regulator of the NAD salvage pathway. CONCLUSIONS/SIGNIFICANCE: The cellular properties of rigidity-dependent cancer cells growing on soft matrices are reminiscent of the properties of dormant cancer cells, e.g., slow growth rate and reduced metabolism. We suggest that the use of relatively soft gels as cell culture substrates would allow molecular pathways to be studied under conditions that reflect the different mechanical environments encountered by cancer cells upon metastasis to distant sites.

Systems analysis of the transcriptional response of human ileocecal epithelial cells to Clostridium difficile toxins and effects on cell cycle control.

BACKGROUND: Toxins A and B (TcdA and TcdB) are Clostridium difficile's principal virulence factors, yet the pathways by which they lead to inflammation and severe diarrhea remain unclear. Also, the relative role of either toxin during infection and the differences in their effects across cell lines is still poorly understood. To better understand their effects in a susceptible cell line, we analyzed the transciptome-wide gene expression response of human ileocecal epithelial cells (HCT-8) after 2, 6, and 24 hr of toxin exposure. RESULTS: We show that toxins elicit very similar changes in the gene expression of HCT-8 cells, with the TcdB response occurring sooner. The high similarity suggests differences between toxins are due to events beyond transcription of a single cell-type and that their relative potencies during infection may depend on differential effects across cell types within the intestine. We next performed an enrichment analysis to determine biological functions associated with changes in transcription. Differentially expressed genes were associated with response to external stimuli and apoptotic mechanisms and, at 24 hr, were predominately associated with cell-cycle control and DNA replication. To validate our systems approach, we subsequently verified a novel G1/S and known G2/M cell-cycle block and increased apoptosis as predicted from our enrichment analysis. CONCLUSIONS: This study shows a successful example of a workflow deriving novel biological insight from transcriptome-wide gene expression. Importantly, we do not find any significant difference between TcdA and TcdB besides potency or kinetics. The role of each toxin in the inhibition of cell growth and proliferation, an important function of cells in the intestinal epithelium, is characterized.

Systems analysis of small signaling modules relevant to eight human diseases.

Using eight newly generated models relevant to addiction, Alzheimer's disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4-25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV's viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.

Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection.

System-level modeling is beginning to be used to decipher high throughput data in the context of disease. In this study, we present an integration of expression microarray data with a genome-scale metabolic reconstruction of Pseudomonas aeruginosa in the context of a chronic cystic fibrosis (CF) lung infection. A genome-scale reconstruction of P. aeruginosa metabolism was tailored to represent the metabolic states of two clonally related lineages of P. aeruginosa isolated from the lungs of a CF patient at different points over a 44-month time course, giving a mechanistic glimpse into how the bacterial metabolism adapts over time in the CF lung. Metabolic capacities were analyzed to determine how tradeoffs between growth and other important cellular processes shift during disease progression. Genes whose knockouts were either significantly growth reducing or lethal in silico were also identified for each time point and serve as hypotheses for future drug targeting efforts specific to the stages of disease progression.

Dephosphorylation of beta-arrestin 1 in glioblastomas.

Beta-Arrestins act as signal terminators for G protein-coupled receptors; they have also been implicated as scaffolding proteins for Src and mitogen-activated protein kinase signaling pathways and transactivators of receptor tyrosine kinases, suggesting their possible role in development and oncogenic signaling. Dephosphorylation of serine 412 is necessary for Src and mitogen-activated protein kinase transactivation. We hypothesized that altered beta-arrestin 1 phosphorylation and activation status could play a role in gliomagenesis. Using monoclonal anti-phospho-(serine 412)- and total beta-arrestin 1 antibodies, we performed immunohistochemistry on 126 human glioma samples and 7 nonneoplastic controls and Western blot analysis on 5 glioblastomas and 5 nonneoplastic controls. We found high constitutive beta-arrestin 1 phosphorylation in nonneoplastic brain tissue, particularly in neurons and neuropil. Most Grade II and III gliomas retained high beta-arrestin 1 phosphorylation. By contrast, most of the glioblastoma samples (58/81) showed nearly complete beta-arrestin 1 dephosphorylation by immunohistochemistry and decreased relative phosphorylation by Western blot. Expression of constitutively activated epidermal growth factor receptor vIII in U251 cells caused decreased beta-arrestin 1 phosphorylation without altering total beta-arrestin 1 levels. These results suggest that beta-arrestin 1 dephosphorylation/inactivation is associated with aspects of the malignant behavior of glioblastomas.