Probing the Genome-Scale Metabolic Landscape of Bordetella pertussis, the Causative Agent of Whooping Cough.

Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with >2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content.IMPORTANCE The metabolic capabilities of Bordetella pertussis, the causative agent of whooping cough, were investigated from a systems-level perspective. We constructed a comprehensive genome-scale metabolic model for B. pertussis and challenged its predictions experimentally. This systems approach shed light on new potential host-microbe interactions and allowed us to rationally design novel growth media with >2-fold improvements in pertussis toxin production. Most importantly, we also uncovered the potential for metabolic flexibility of B. pertussis (significantly larger range of substrates than previously alleged; novel active pathways allowing growth in minimal, nearly mineral nutrient combinations where only the carbon source must be organic), although our results also highlight the importance of strain-specific regulatory determinants in shaping metabolic capabilities. Deciphering the underlying regulatory mechanisms appears to be crucial for a comprehensive understanding of B. pertussis's lifestyle and the epidemiology of whooping cough. The contribution of metabolic models in this context will require the extension of the genome-scale metabolic model to integrate this regulatory dimension.

PAPD5-mediated 3' adenylation and subsequent degradation of miR-21 is disrupted in proliferative disease.

Next-generation sequencing experiments have shown that microRNAs (miRNAs) are expressed in many different isoforms (isomiRs), whose biological relevance is often unclear. We found that mature miR-21, the most widely researched miRNA because of its importance in human disease, is produced in two prevalent isomiR forms that differ by 1 nt at their 3' end, and moreover that the 3' end of miR-21 is posttranscriptionally adenylated by the noncanonical poly(A) polymerase PAPD5. PAPD5 knockdown caused an increase in the miR-21 expression level, suggesting that PAPD5-mediated adenylation of miR-21 leads to its degradation. Exoribonuclease knockdown experiments followed by small-RNA sequencing suggested that PARN degrades miR-21 in the 3'-to-5' direction. In accordance with this model, microarray expression profiling demonstrated that PAPD5 knockdown results in a down-regulation of miR-21 target mRNAs. We found that disruption of the miR-21 adenylation and degradation pathway is a general feature in tumors across a wide range of tissues, as evidenced by data from The Cancer Genome Atlas, as well as in the noncancerous proliferative disease psoriasis. We conclude that PAPD5 and PARN mediate degradation of oncogenic miRNA miR-21 through a tailing and trimming process, and that this pathway is disrupted in cancer and other proliferative diseases.

Systemic identification of estrogen-regulated genes in breast cancer cells through cap analysis of gene expression mapping.

To explore the estrogen-regulated genes genome-widely in breast cancer, cap analysis of gene expression (CAGE) sequencing was performed in MCF-7 cells under estrogen treatment. Estrogen-regulated expressional changes were found in 1537 CAGE tag clusters (TCs) (⩾1.5 or ⩽0.66-folds). Among them, 15 TCs were situated in the vicinity of (⩽10 kb) reported estrogen receptor-binding sites. Knockdown experiments of the 15 TC-associated genes demonstrated that the genes such as RAMP3, ISOC1 and GPRC5C potentially regulate the growth or migration of MCF-7 cells. These results suggest that CAGE sequencing will reveal novel estrogen target genes in breast cancer.

A data integration and visualization resource for the metabolic network of Synechocystis sp. PCC 6803.

Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight in addition to statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis sp. PCC 6803, an important model cyanobacterium for sustainable biofuel production. We illustrate how this metabolic map can be used to integrate experimental and computational data for Synechocystis sp. PCC 6803 systems biology and metabolic engineering studies. Additionally, we discuss how this map, and the software infrastructure that we supply with it, can be used in the development of other organism-specific metabolic network visualizations. In addition to the Python console package VoNDA (http://vonda.sf.net), we provide a working demonstration of the interactive metabolic map and the associated Synechocystis sp. PCC 6803 genome-scale stoichiometric model, as well as various ready-to-visualize microarray data sets, at http://f-a-m-e.org/synechocytis.

FAME, the Flux Analysis and Modeling Environment.

BACKGROUND: The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists. RESULTS: The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at http://f-a-m-e.org/. CONCLUSIONS: With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.

A practical guide to genome-scale metabolic models and their analysis.

Genome-scale metabolic reconstructions and their analysis with constraint-based modeling techniques have gained enormous momentum. It is a natural next step after sequencing of a genome, as a technique that links top-down systems biology analyses at genome scale with bottom-up systems biology modeling scrutiny. This chapter aims at (systems) biologists that have an interest in, but no extensive knowledge of, applying genome-scale metabolic reconstruction and modeling to their organism. Rather than being comprehensive--excellent and extensive reviews exist on every aspect of this field--we give a rather personal account on our experience with the process of reconstruction and modeling. First, we place genome-scale metabolic models in the spectrum of modeling approaches, and rather extensively discuss, for nonexperts, the central concept in constraint-based modeling: the solution space that is bounded through constraints on fluxes. We subsequently provide an overview of the different steps involved in metabolic reconstruction and modeling, pointing to aspects that we found difficult, important, not well enough addressed in the current reviews, or any combination thereof. In this way, we hope that this chapter serves as a practical guide through the field.