Cystic fibrosis (CF) results in inflammation, malabsorption of fats and other nutrients, and obstruction in the gastrointestinal (GI) tract, yet the mechanisms linking these disease manifestations to microbiome composition remain largely unexplored. Here we used metagenomic analysis to systematically characterize fecal microbiomes of children with and without CF, demonstrating marked CF-associated taxonomic dysbiosis and functional imbalance. We further showed that these taxonomic and functional shifts were especially pronounced in young children with CF and diminished with age. Importantly, the resulting dysbiotic microbiomes had significantly altered capacities for lipid metabolism, including decreased capacity for overall fatty acid biosynthesis and increased capacity for degrading anti-inflammatory short-chain fatty acids. Notably, these functional differences correlated with fecal measures of fat malabsorption and inflammation. Combined, these results suggest that enteric fat abundance selects for pro-inflammatory GI microbiota in young children with CF, offering novel strategies for improving the health of children with CF-associated fat malabsorption.

BACKGROUND:Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.RESULTS:NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms' niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.CONCLUSIONS:The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.

Within each bacterial species, different strains may vary in the set of genes they encode or in the copy number of these genes. Yet, taxonomic characterization of the human microbiota is often limited to the species level or to previously sequenced strains, and accordingly, the prevalence of intra-species variation, its functional role, and its relation to host health remain unclear. Here, we present a comprehensive large-scale analysis of intra-species copy-number variation in the gut microbiome, introducing a rigorous computational pipeline for detecting such variation directly from shotgun metagenomic data. We uncover a large set of variable genes in numerous species and demonstrate that this variation has significant functional and clinically relevant implications. We additionally infer intra-species compositional profiles, identifying population structure shifts and the presence of yet uncharacterized variants. Our results highlight the complex relationship between microbiome composition and functional capacity, linking metagenome-level compositional shifts to strain-level variation.

To assess the functional capacities of microbial communities, including those inhabiting the human body, shotgun metagenomic reads are often aligned to a database of known genes. Such homology-based annotation practices critically rely on the assumption that short reads can map to orthologous genes of similar function. This assumption, however, and the various factors that impact short read annotation, have not been systematically evaluated. To address this challenge, we generated an extremely large database of simulated reads (totaling 15.9 Gb), spanning over 500,000 microbial genes and 170 curated genomes and including, for many genomes, every possible read of a given length. We annotated each read using common metagenomic protocols, fully characterizing the effect of read length, sequencing error, phylogeny, database coverage, and mapping parameters. We additionally rigorously quantified gene-, genome-, and protocol-specific annotation biases. Overall, our findings provide a first comprehensive evaluation of the capabilities and limitations of functional metagenomic annotation, providing crucial goal-specific best-practice guidelines to inform future metagenomic research.

The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically relevant and ecologically relevant insights from the rich trove of data now available.

Metagenomics has transformed our understanding of the microbial world, allowing researchers to bypass the need to isolate and culture individual taxa and to directly characterize both the taxonomic and gene compositions of environmental samples. However, associating the genes found in a metagenomic sample with the specific taxa of origin remains a critical challenge. Existing binning methods, based on nucleotide composition or alignment to reference genomes allow only a coarse-grained classification and rely heavily on the availability of sequenced genomes from closely related taxa. Here, we introduce a novel computational framework, integrating variation in gene abundances across multiple samples with taxonomic abundance data to deconvolve metagenomic samples into taxa-specific gene profiles and to reconstruct the genomic content of community members. This assembly-free method is not bounded by various factors limiting previously described methods of metagenomic binning or metagenomic assembly and represents a fundamentally different approach to metagenomic-based genome reconstruction. An implementation of this framework is available at http://elbo.gs.washington.edu/software.html. We first describe the mathematical foundations of our framework and discuss considerations for implementing its various components. We demonstrate the ability of this framework to accurately deconvolve a set of metagenomic samples and to recover the gene content of individual taxa using synthetic metagenomic samples. We specifically characterize determinants of prediction accuracy and examine the impact of annotation errors on the reconstructed genomes. We finally apply metagenomic deconvolution to samples from the Human Microbiome Project, successfully reconstructing genus-level genomic content of various microbial genera, based solely on variation in gene count. These reconstructed genera are shown to correctly capture genus-specific properties. With the accumulation of metagenomic data, this deconvolution framework provides an essential tool for characterizing microbial taxa never before seen, laying the foundation for addressing fundamental questions concerning the taxa comprising diverse microbial communities.

: Army installations contain a high density of mission-critical buildings that require constant protection from accidental or deliberate contamination of water distribution systems. Current simulations of contaminant fate and transport in pipe systems do not accurately portray reality. The simulations assume pure hydraulic transport of contaminants and do not account for sorption of the contaminant on pipe walls. Additionally, subsequent reactions such as hydrolysis are not considered. These omissions reduce predictability of a contaminant s progression and effect in the distribution system. In addition, inadequate understanding of contaminant fate and transport may result in an unacceptable risk to mission readiness. However, performing laboratory tests for every known and emerging chemical or biological contaminant to obtain uptake and reaction parameters is not feasible with regard to time or cost investments. This report documents advances in molecular modeling predictions for the transport of contaminants using: the Nanoscale Molecular Dynamics program, the influence of bacterial biofilms on spore viability within a chlorinated water distribution system, the computational chemistry predictions of the rate of hydrolysis of a specific contaminant, and the inclusion of results into the pre-dictive software, EPANet. This work opens the way for better vulnerability assessments, protection, real-time response, remediation, and planning.

Molecular dynamics (MD) simulations have become a standard method for the rational design and interpretation of experimental studies of DNA translocation through nanopores. The MD method, however, offers a multitude of algorithms, parameters, and other protocol choices that can affect the accuracy of the resulting data as well as computational efficiency. In this chapter, we examine the most popular choices offered by the MD method, seeking an optimal set of parameters that enable the most computationally efficient and accurate simulations of DNA and ion transport through biological nanopores. In particular, we examine the influence of short-range cutoff, integration timestep and force field parameters on the temperature and concentration dependence of bulk ion conductivity, ion pairing, ion solvation energy, DNA structure, DNA-ion interactions, and the ionic current through a nanopore.

UNLABELLED:NetSeed is a web tool and Perl module for analyzing the topology of metabolic networks and calculating the set of exogenously acquired compounds. NetSeed is based on the seed detection algorithm, developed and validated in previous studies. AVAILABILITY:The NetSeed web-based tool, open-source Perl module, examples and documentation are freely available online at: http://depts.washington.edu/elbogs/NetSeed.

Adsorption of dissolved molecules onto solid surfaces can be extremely sensitive to the atomic-scale properties of the solute and surface, causing difficulties for the design of fluidic systems in industrial, medical and technological applications. In this communication, we show that the Langmuir isotherm for adsorption of a small molecule to a realistic, heterogeneous surface can be predicted from atomic structures of the molecule and surface through molecular dynamics (MD) simulations. We highlight the method by studying the adsorption of dimethyl-methylphosphonate (DMMP) to amorphous silica substrates and show that subtle differences in the atomic-scale surface properties can have drastic effects on the Langmuir isotherm. The sensitivity of the method presented is sufficient to permit the optimization of fluidic devices and to determine fundamental design rules for controlling adsorption at the nanoscale.

Modeling the transport of solutes through fluidic systems that have adsorbing surfaces is challenging due to the range of length and time scales involved. The components of such systems typically have dimensions from hundreds of nanometres to microns, whereas adsorption of solutes is sensitive to the atomic-scale structure of the solutes and surfaces. Here, we describe an atomic-resolution Brownian dynamics method for modeling the transport of solutes through sticky nanofluidic channels. Our method can fully recreate the results of all-atom molecular dynamics simulations at a fraction of the computational cost of the latter, which makes simulations of micron-size channels at a millisecond time scale possible without losing information about the atomic-scale features of the system. We demonstrate the capability of our method by simulating the rise and fall of solute concentration in sub-micron-long sticky nanochannels, showing that the atomic-scale features of the channels' surfaces have a dramatic effect on the kinetics of solute transport in and out of the channels. We expect our method to find applications in design and optimization of micro and nanofluidic systems for solute-specific transport and to complement existing approaches to modeling lab-on-a-chip devices by providing atomic scale information at a low computational cost.

Reducing the size of a nanofluidic channel not only creates new opportunities for high-precision manipulation of biological macromolecules but also makes the performance of the entire nanofluidic system more susceptible to undesirable interactions between the transported biomolecules and the walls of the channel. In this paper, we report molecular dynamics simulations of pressure-driven flow through a silica nanochannel and characterize, with atomic resolution, adsorption of a model protein to the surface of the nanochannel. Although the simulated adsorption of the proteins was found to be nonspecific, it had a dramatic effect on the rate of the protein transport. To determine the relative strength of the protein-silica interactions in different adsorbed states, we simulated flow-induced desorption of the proteins from the silica surface. Our analysis of the protein conformations in the adsorbed states did not reveal any simple dependence of the adsorption strength on the size and composition of the protein-silica contact, suggesting that the heterogeneity of the silica surface may be an important factor.

AbstractImmunoassays for detection of bacterial pathogens rely on the selectivity and stability of bio‐recognition elements such as antibodies tethered to sensor surfaces. The search for novel surfaces that improve the stability of biomolecules and assay performance has been pursued for a long time. However, the anticipated improvements in stability have not been realized in practice under physiological conditions because the surface functionalization layers on commonly used substrates, silica and gold, are themselves unstable on time scales of days. In this paper, we show that covalent linking of antibodies to diamond surfaces leads to substantial improvements in biological activity of proteins as measured by the ability to selectively capture cells of the pathogenic bacterium Escherichia coli O157:H7 even after exposure to buffer solutions at 37 ºC for extended periods of time, approaching 2 weeks. Our results from ELISA, XPS, fluorescence microscopy, and MD simulations suggest that by using highly stable surface chemistry and controlling the nanoscale organization of the antibodies on the surface, it is possible to achieve significant improvements in biological activity and stability. Our findings can be easily extended to functionalization of micro and nanodimensional sensors and structures of biomedical diagnostic and therapeutic interest.

Immunoassays for detection of bacterial pathogens rely on the selectivity and stability of bio‐recognition elements such as antibodies tethered to sensor surfaces. The search for novel surfaces that improve the stability of biomolecules and assay performance has been pursued for a long time. However, the anticipated improvements in stability have not been realized in practice under physiological conditions because the surface functionalization layers on commonly used substrates, silica and gold, are themselves unstable on time scales of days. In this paper, we show that covalent linking of antibodies to diamond surfaces leads to substantial improvements in biological activity of proteins as measured by the ability to selectively capture cells of the pathogenic bacterium Escherichia coli O157:H7 even after exposure to buffer solutions at 37 ºC for extended periods of time, approaching 2 weeks. Our results from ELISA, XPS, fluorescence microscopy, and MD simulations suggest that by using highly stable surface chemistry and controlling the nanoscale organization of the antibodies on the surface, it is possible to achieve significant improvements in biological activity and stability. Our findings can be easily extended to functionalization of micro and nanodimensional sensors and structures of biomedical diagnostic and therapeutic interest.