Fusion of genomic, proteomic and phenotypic data: the case of potyviruses.

Mol Biosyst. 2016 Jan;12(1):253-61

Authors: Folch-Fortuny A, Bosque G, Picó J, Ferrer A, Elena SF

Abstract
Data fusion has been widely applied to analyse different sources of information, combining all of them in a single multivariate model. This methodology is mandatory when different omic data sets must be integrated to fully understand an organism using a systems biology approach. Here, a data fusion procedure is presented to combine genomic, proteomic and phenotypic data sets gathered for Tobacco etch virus (TEV). The genomic data correspond to random mutations inserted in most viral genes. The proteomic data represent both the effect of these mutations on the encoded proteins and the perturbation induced by the mutated proteins to their neighbours in the protein-protein interaction network (PPIN). Finally, the phenotypic trait evaluated for each mutant virus is replicative fitness. To analyse these three sources of information a Partial Least Squares (PLS) regression model is fitted in order to extract the latent variables from data that explain (and relate) the significant variables to the fitness of TEV. The final output of this methodology is a set of functional modules of the PPIN relating topology and mutations with fitness. Throughout the re-analysis of these diverse TEV data, we generated valuable information on the mechanism of action of certain mutations and how they translate into organismal fitness. Results show that the effect of some mutations goes beyond the protein they directly affect and spreads on the PPIN to neighbour proteins, thus defining functional modules.

PMID: 26593691 [PubMed - indexed for MEDLINE]

A termination criterion for parameter estimation in stochastic models in systems biology.

Biosystems. 2015 Nov;137:55-63

Authors: Zimmer C, Sahle S

Abstract
Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model.

PMID: 26360409 [PubMed - indexed for MEDLINE]

A Rich-Club Organization in Brain Ischemia Protein Interaction Network.

Sci Rep. 2015;5:13513

Authors: Alawieh A, Sabra Z, Sabra M, Tomlinson S, Zaraket FA

Abstract
Ischemic stroke involves multiple pathophysiological mechanisms with complex interactions. Efforts to decipher those mechanisms and understand the evolution of cerebral injury is key for developing successful interventions. In an innovative approach, we use literature mining, natural language processing and systems biology tools to construct, annotate and curate a brain ischemia interactome. The curated interactome includes proteins that are deregulated after cerebral ischemia in human and experimental stroke. Network analysis of the interactome revealed a rich-club organization indicating the presence of a densely interconnected hub structure of prominent contributors to disease pathogenesis. Functional annotation of the interactome uncovered prominent pathways and highlighted the critical role of the complement and coagulation cascade in the initiation and amplification of injury starting by activation of the rich-club. We performed an in-silico screen for putative interventions that have pleiotropic effects on rich-club components and we identified estrogen as a prominent candidate. Our findings show that complex network analysis of disease related interactomes may lead to a better understanding of pathogenic mechanisms and provide cost-effective and mechanism-based discovery of candidate therapeutics.

PMID: 26310627 [PubMed - indexed for MEDLINE]

Circadian systems biology in Metazoa.

Brief Bioinform. 2015 Nov;16(6):1008-24

Authors: Lin LL, Huang HC, Juan HF, 2014 Taida Cancer Systems Biology Study Group

Abstract
Systems biology, which can be defined as integrative biology, comprises multistage processes that can be used to understand components of complex biological systems of living organisms and provides hierarchical information to decoding life. Using systems biology approaches such as genomics, transcriptomics and proteomics, it is now possible to delineate more complicated interactions between circadian control systems and diseases. The circadian rhythm is a multiscale phenomenon existing within the body that influences numerous physiological activities such as changes in gene expression, protein turnover, metabolism and human behavior. In this review, we describe the relationships between the circadian control system and its related genes or proteins, and circadian rhythm disorders in systems biology studies. To maintain and modulate circadian oscillation, cells possess elaborative feedback loops composed of circadian core proteins that regulate the expression of other genes through their transcriptional activities. The disruption of these rhythms has been reported to be associated with diseases such as arrhythmia, obesity, insulin resistance, carcinogenesis and disruptions in natural oscillations in the control of cell growth. This review demonstrates that lifestyle is considered as a fundamental factor that modifies circadian rhythm, and the development of dysfunctions and diseases could be regulated by an underlying expression network with multiple circadian-associated signals.

PMID: 25758249 [PubMed - indexed for MEDLINE]

Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.

Cell Discov. 2016;2:16025

Authors: Lee JH, Zhao XM, Yoon I, Lee JY, Kwon NH, Wang YY, Lee KM, Lee MJ, Kim J, Moon HG, In Y, Hao JK, Park KM, Noh DY, Han W, Kim S

Abstract
Despite the explosion in the numbers of cancer genomic studies, metastasis is still the major cause of cancer mortality. In breast cancer, approximately one-fifth of metastatic patients survive 5 years. Therefore, detecting the patients at a high risk of developing distant metastasis at first diagnosis is critical for effective treatment strategy. We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequencing of our collected 78 normal-paired breast cancers. Unlike driver mutations occurring commonly in cancers as reported in the literature, the mutations detected here are relatively rare mutations occurring in less than half metastatic samples. By supposing that the driver mutations should affect the metastasis gene signatures, we develop a novel computational pipeline to identify the driver mutations that affect transcription factors regulating metastasis gene signatures. We identify driver mutations in ADPGK, NUP93, PCGF6, PKP2 and SLC22A5, which are verified to enhance cancer cell migration and prompt metastasis with in vitro experiments. The discovered somatic mutations may be helpful for identifying patients who are likely to develop distant metastasis.

PMID: 27625789 [PubMed]

Biological Networks for Cancer Candidate Biomarkers Discovery.

Cancer Inform. 2016;15(Suppl 3):1-7

Authors: Yan W, Xue W, Chen J, Hu G

Abstract
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

PMID: 27625573 [PubMed]

Evaluation of O2PLS in Omics data integration.

BMC Bioinformatics. 2016;17 Suppl 2:11

Authors: Bouhaddani SE, Houwing-Duistermaat J, Salo P, Perola M, Jongbloed G, Uh HW

Abstract
BACKGROUND: Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.
RESULTS: A simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.
CONCLUSIONS: Simulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

PMID: 26822911 [PubMed - indexed for MEDLINE]

Optimality in the zonation of ammonia detoxification in rodent liver.

Arch Toxicol. 2015 Nov;89(11):2069-78

Authors: Bartl M, Pfaff M, Ghallab A, Driesch D, Henkel SG, Hengstler JG, Schuster S, Kaleta C, Gebhardt R, Zellmer S, Li P

Abstract
The rodent liver eliminates toxic ammonia. In mammals, three enzymes (or enzyme systems) are involved in this process: glutaminase, glutamine synthetase and the urea cycle enzymes, represented by carbamoyl phosphate synthetase. The distribution of these enzymes for optimal ammonia detoxification was determined by numerical optimization. This in silico approach predicted that the enzymes have to be zonated in order to achieve maximal removal of toxic ammonia and minimal changes in glutamine concentration. Using 13 compartments, representing hepatocytes, the following predictions were generated: glutamine synthetase is active only within a narrow pericentral zone. Glutaminase and carbamoyl phosphate synthetase are located in the periportal zone in a non-homogeneous distribution. This correlates well with the paradoxical observation that in a first step glutamine-bound ammonia is released (by glutaminase) although one of the functions of the liver is detoxification by ammonia fixation. The in silico approach correctly predicted the in vivo enzyme distributions also for non-physiological conditions (e.g. starvation) and during regeneration after tetrachloromethane (CCl4) intoxication. Metabolite concentrations of glutamine, ammonia and urea in each compartment, representing individual hepatocytes, were predicted. Finally, a sensitivity analysis showed a striking robustness of the results. These bioinformatics predictions were validated experimentally by immunohistochemistry and are supported by the literature. In summary, optimization approaches like the one applied can provide valuable explanations and high-quality predictions for in vivo enzyme and metabolite distributions in tissues and can reveal unknown metabolic functions.

PMID: 26438405 [PubMed - indexed for MEDLINE]

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

Astrobiology. 2016 Sep;16(9):677-689

Authors: Roy R, Shilpa PP, Bagh S

Abstract
Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level.
KEY WORDS: Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

PMID: 27623197 [PubMed - as supplied by publisher]

Origin of a folded repeat protein from an intrinsically disordered ancestor.

Elife. 2016 Sep 13;5

Authors: Zhu H, Sepulveda E, Hartmann MD, Kogenaru M, Ursinus A, Sulz E, Albrecht R, Coles M, Martin J, Lupas AN

Abstract
Repetitive proteins are thought to have arisen through the amplification of subdomain-sized peptides. Many of these originated in a non-repetitive context as cofactors of RNA-based replication and catalysis, and required the RNA to assume their active conformation. In search of the origins of one of the most widespread repeat protein families, the tetratricopeptide repeat (TPR), we identified several potential homologs of its repeated helical hairpin in non-repetitive proteins, including the putatively ancient ribosomal protein S20 (RPS20), which only becomes structured in the context of the ribosome. We evaluated the ability of the RPS20 hairpin to form a TPR fold by amplification and obtained structures identical to natural TPRs for variants with 2-5 point mutations per repeat. The mutations were neutral in the parent organism, suggesting that they could have been sampled in the course of evolution. TPRs could thus have plausibly arisen by amplification from an ancestral helical hairpin.

PMID: 27623012 [PubMed - as supplied by publisher]

A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution.

Elife. 2016 Sep 13;5

Authors: Zhou X, Blocker AW, Airoldi EM, O'Shea EK

Abstract
Understanding chromatin function requires knowing the precise location of nucleosomes. MNase-seq methods have been widely applied to characterize nucleosome organization in vivo, but generally lack the accuracy to determine the precise nucleosome positions. Here we develop a computational approach leveraging digestion variability to determine nucleosome positions at base-pair resolution from MNase-seq data. We generate a variability template as a simple error model for how MNase digestion affects mapping of individual nucleosomes. Applied to both yeast and human cells, this analysis reveals that alternatively positioned nucleosomes are prevalent and create significant heterogeneity in a cell population. We show that the periodic occurrences of dinucleotide sequences relative to nucleosome dyads can be directly determined from genome-wide nucleosome positions from MNase-seq. Alternatively positioned nucleosomes near transcription start sites likely represent different states of promoter nucleosomes during transcription initiation. Our method can be applied to map nucleosome positions in diverse organisms at base-pair resolution.

PMID: 27623011 [PubMed - as supplied by publisher]

Plant-Derived Terpenes: A Feedstock for Specialty Biofuels.

Trends Biotechnol. 2016 Sep 9;

Authors: Mewalal R, Rai DK, Kainer D, Chen F, Külheim C, Peter GF, Tuskan GA

Abstract
Research toward renewable and sustainable energy has identified specific terpenes capable of supplementing or replacing current petroleum-derived fuels. Despite being naturally produced and stored by many plants, there are few examples of commercial recovery of terpenes from plants because of low yields. Plant terpene biosynthesis is regulated at multiple levels, leading to wide variability in terpene content and chemistry. Advances in the plant molecular toolkit, including annotated genomes, high-throughput omics profiling, and genome editing, have begun to elucidate plant terpene metabolism, and such information is useful for bioengineering metabolic pathways for specific terpenes. We review here the status of terpenes as a specialty biofuel and discuss the potential of plants as a viable agronomic solution for future terpene-derived biofuels.

PMID: 27622303 [PubMed - as supplied by publisher]

Metabolic flux control in glycosylation.

Curr Opin Struct Biol. 2016 Sep 9;40:97-103

Authors: McDonald AG, Hayes JM, Davey GP

Abstract
Glycosylation is a common post-translational protein modification, in which glycans are built onto proteins through the sequential addition of monosaccharide units, in reactions catalysed by glycosyltransferases. Glycosylation influences the physicochemical and biological properties of proteins, with subsequent effects on subcellular and extracellular protein trafficking, cell-cell recognition, and ligand-receptor interactions. Glycan structures can be complex, as is the regulation of their biosynthesis, and it is only recently that the systems biology of metabolic flux control and glycosyltransferase networks has become a study in its own right. We review various models of glycosylation that have been proposed to date, based on current knowledge of Golgi structure and function, and consider how metabolic flux through glycosyltransferase networks regulates glycosylation events in the cell.

PMID: 27620650 [PubMed - as supplied by publisher]

Formal Derivation of Qualitative Dynamical Models from Biochemical Networks.

Biosystems. 2016 Sep 9;

Authors: Abou-Jaoudé W, Thieffry D, Feret J

Abstract
As technological advances allow a better identification of cellular networks, large-scale molecular data are swiftly produced, allowing the construction of large and detailed molecular interaction maps. One approach to unravel the dynamical properties of such complex systems consists in deriving coarse-grained dynamical models from these maps, which would make the salient properties emerge. We present here a method to automatically derive such models, relying on the abstract interpretation framework to formally relate model behaviour at different levels of description. We illustrate our approach on two relevant case studies: the formation of a complex involving a protein adaptor, and a race between two competing biochemical reactions. States and traces of reaction networks are first abstracted by sampling the number of instances of chemical species within a finite set of intervals. We show that the qualitative models induced by this abstraction are too coarse to reproduce properties of interest. We then refine our approach by taking into account additional constraints, the mass invariants and the limiting resources for interval crossing, and by introducing information on the reaction kinetics. The resulting qualitative models are able to capture sophisticated properties of interest, such as a sequestration effect, which arise in the case studies and, more generally, participate in shaping the dynamics of cell signaling and regulatory networks. Our methodology offers new trade-offs between complexity and accuracy, and clarifies the implicit assumptions made in the process of qualitative modelling of biological networks.

PMID: 27619217 [PubMed - as supplied by publisher]

Retrieving relevant time-course experiments: a study on Arabidopsis microarrays.

IET Syst Biol. 2016 Jun;10(3):87-93

Authors: Şener DD, Oğul H

Abstract
Understanding time-course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta-analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time-course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time-course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time-course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.

PMID: 27187987 [PubMed - indexed for MEDLINE]

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model.

BMC Bioinformatics. 2016;17 Suppl 1:9

Authors: Chen L, Cai C, Chen V, Lu X

Abstract
BACKGROUND: A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.
RESULTS: We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation.
CONCLUSIONS: Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

PMID: 26818848 [PubMed - indexed for MEDLINE]

D-VASim - An Interactive Virtual Laboratory Environment for the Simulation and Analysis of Genetic Circuits.

Bioinformatics. 2016 Sep 11;

Authors: Baig H, Madsen J

Abstract
Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model.
AVAILABILITY: D-VASim is available for Windows and Mac OS and can be downloaded from bda.compute.dtu.dk/downloads/ CONTACT: haba@dtu.dk, jama@dtu.dk.

PMID: 27616709 [PubMed - as supplied by publisher]

Biomarkers, Early Diagnosis, and Clinical Predictors of Bronchopulmonary Dysplasia.

Clin Perinatol. 2015 Dec;42(4):739-54

Authors: Lal CV, Ambalavanan N

Abstract
The pathogenesis of bronchopulmonary dysplasia (BPD) is multifactorial, and the clinical phenotype of BPD is extremely variable. Several clinical and laboratory biomarkers have been proposed for the early identification of infants at higher risk of BPD and for determination of prognosis of infants with a diagnosis of BPD. The authors review available literature on prediction tools and biomarkers of BPD, using clinical variables and biomarkers based on imaging, lung function measures, and measurements of various analytes in different body fluids that have been determined to be associated with BPD either in a targeted manner or by unbiased omic profiling.

PMID: 26593076 [PubMed - indexed for MEDLINE]

Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example.

Pharmacogenomics. 2015 Nov;16(17):1975-88

Authors: Mas S, Gassó P, Lafuente A

Abstract
Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.

PMID: 26556470 [PubMed - indexed for MEDLINE]

Tracking the Dynamic Relationship between Cellular Systems and Extracellular Subproteomes in Pseudomonas aeruginosa Biofilms.

J Proteome Res. 2015 Nov 6;14(11):4524-37

Authors: Park AJ, Murphy K, Surette MD, Bandoro C, Krieger JR, Taylor P, Khursigara CM

Abstract
The transition of the opportunistic pathogen Pseudomonas aeruginosa from free-living bacteria into surface-associated biofilm communities represents a viable target for the prevention and treatment of chronic infectious disease. We have established a proteomics platform that identified 2443 and 1142 high-confidence proteins in P. aeruginosa whole cells and outer-membrane vesicles (OMVs), respectively, at three time points during biofilm development (ProteomeXchange identifier PXD002605). The analysis of cellular systems, specifically the phenazine biosynthetic pathway, demonstrates that whole-cell protein abundance correlates to end product (i.e., pyocyanin) concentrations in biofilm but not in planktonic cultures. Furthermore, increased cellular protein abundance in this pathway results in quantifiable pyocyanin in early biofilm OMVs and OMVs from both growth modes isolated at later time points. Overall, our data indicate that the OMVs being released from the surface of the biofilm whole cells have unique proteomes in comparison to their planktonic counterparts. The relative abundance of OMV proteins from various subcellular sources showed considerable differences between the two growth modes over time, supporting the existence and preferential activation of multiple OMV biogenesis mechanisms under different conditions. The consistent detection of cytoplasmic proteins in all of the OMV subproteomes challenges the notion that OMVs are composed of outer membrane and periplasmic proteins alone. Direct comparisons of outer-membrane protein abundance levels between OMVs and whole cells shows ratios that vary greatly from 1:1 and supports previous studies that advocate the specific inclusion, or "packaging", of proteins into OMVs. The quantitative analysis of packaged protein groups suggests biogenesis mechanisms that involve untethered, rather than absent, peptidoglycan-binding proteins. Collectively, individual protein and biological system analyses of biofilm OMVs show that drug-binding cytoplasmic proteins and porins are potentially shuttled from the whole cell into the OMVs and may contribute to the antibiotic resistance of P. aeruginosa whole cells within biofilms.

PMID: 26378716 [PubMed - indexed for MEDLINE]