More than just a gut feeling : constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.Peer reviewe

Green genes: bioinformatics and systems-biology innovations drive algal biotechnology.

Many species of microalgae produce hydrocarbons, polysaccharides, and other valuable products in significant amounts. However, large-scale production of algal products is not yet competitive against non-renewable alternatives from fossil fuel. Metabolic engineering approaches will help to improve productivity, but the exact metabolic pathways and the identities of the majority of the genes involved remain unknown. Recent advances in bioinformatics and systems-biology modeling coupled with increasing numbers of algal genome-sequencing projects are providing the means to address this. A multidisciplinary integration of methods will provide synergy for a systems-level understanding of microalgae, and thereby accelerate the improvement of industrially valuable strains. In this review we highlight recent advances and challenges to microalgal research and discuss future potential.We acknowledge support from the EU FP7 project SPLASH (Sustainable PoLymers from Algae Sugars and Hydrocarbons), grant agreement number 311956.This is the accepted manuscript. The final version is available from Cell/Elsevier at http://www.sciencedirect.com/science/article/pii/S016777991400196

A Multi-Platform Flow Device for Microbial (Co-) Cultivation and Microscopic Analysis

Novel microbial cultivation platforms are of increasing interest to researchers in academia and industry. The development of materials with specialized chemical and geometric properties has opened up new possibilities in the study of previously unculturable microorganisms and has facilitated the design of elegant, high-throughput experimental set-ups. Within the context of the international Genetically Engineered Machine (iGEM) competition, we set out to design, manufacture, and implement a flow device that can accommodate multiple growth platforms, that is, a silicon nitride based microsieve and a porous aluminium oxide based microdish. It provides control over (co-)culturing conditions similar to a chemostat, while allowing organisms to be observed microscopically. The device was designed to be affordable, reusable, and above all, versatile. To test its functionality and general utility, we performed multiple experiments with Escherichia coli cells harboring synthetic gene circuits and were able to quantitatively study emerging expression dynamics in real-time via fluorescence microscopy. Furthermore, we demonstrated that the device provides a unique environment for the cultivation of nematodes, suggesting that the device could also prove useful in microscopy studies of multicellular microorganisms

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Multiple Scenario Generation of Subsurface Models:Consistent Integration of Information from Geophysical and Geological Data throuh Combination of Probabilistic Inverse Problem Theory and Geostatistics

Neutrinos with energies above 1017 eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos of all flavors interacting in the atmosphere at large zenith angles, as well as for Earth-skimming \u3c4 neutrinos with nearly tangential trajectories relative to the Earth. No neutrino candidates were found in 3c 14.7 years of data taken up to 31 August 2018. This leads to restrictive upper bounds on their flux. The 90% C.L. single-flavor limit to the diffuse flux of ultra-high-energy neutrinos with an E\u3bd-2 spectrum in the energy range 1.0 7 1017 eV -2.5 7 1019 eV is E2 dN\u3bd/dE\u3bd < 4.4 7 10-9 GeV cm-2 s-1 sr-1, placing strong constraints on several models of neutrino production at EeV energies and on the properties of the sources of ultra-high-energy cosmic rays

Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping

Performance evaluation of COMMGEN.

<p>(<b>a</b>) Evaluation of GSM ability to predict growth phenotypes. Predictive ability of initial GSMs (blue), basic consensus models (red), and automatically created refined consensus model (green) according to the metrics defined in the text. The test data comprised gene knockout data (<i>B</i>. <i>subtilis</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref003" target="_blank">3</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref036" target="_blank">36</a>], <i>P</i>. <i>putida</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref008" target="_blank">8</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref050" target="_blank">50</a>], <i>M</i>. <i>tuberculosis</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref051" target="_blank">51</a>], <i>S</i>. <i>cerevisiae</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref049" target="_blank">49</a>]), biolog data (<i>B</i>. <i>subtilis</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref003" target="_blank">3</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref036" target="_blank">36</a>], <i>P</i>. <i>putida</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref008" target="_blank">8</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref033" target="_blank">33</a>]) and auxotrophies (<i>P</i>. <i>putida</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref050" target="_blank">50</a>]). See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.s004" target="_blank">S3 Protocol</a> for details. (<b>b,c</b>) Comparison of manual (yeast consensus model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref020" target="_blank">20</a>] based on the IGSMs iMM904 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref037" target="_blank">37</a>] and iLL672 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005085#pcbi.1005085.ref038" target="_blank">38</a>]) and automatic consensus model generation with namespace matching only, or with COMMGEN. (<b>b</b>) Numbers of common reactions and metabolites for manual curation, name space conversion, and automatically created refined consensus model. (<b>c</b>) Incidences of inconsistent reaction classes identified by COMMGEN.</p