Distributions of alternative metrics for correct and randomized orders of colonization.

<p>(A) Similar to what shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g001" target="_blank">Fig. 1B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>, we computed inter-species distance between organisms along paths that respect (red) or do not respect (blue) the layered order of colonization of the Kolenbrander map. Here, however, as opposed to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>, we compute the Jaccard distance between two species based on their profiles of non-enzyme genes (as identifiable through KEGG KO numbers). (B) The correct and incorrect orders of colonization are compared based on an information metric, rather than on the Jaccard distance. In walking along a colonization order path from one organism to the next, we compute (in Nats) the amount of information added due to the presence of previously absent enzymes. The added information for each pair of adjacent organisms is summed to form the added information score, along paths that respect (red) or do not respect (blue) the layered order of colonization. The purple distribution is obtained by computing the added information scores for orders of colonization that reflect the layered structure, but walk through it in reverse order (i.e. from the outer layer downwards towards the salivary pellicle).</p

Expected synergy between metabolic networks as a function of metabolic distance.

<p>The synergy is computed as the count of elementary flux modes (pathways) that are feasible for a metabolic network that is the union of two networks with a given Jaccard’s distance from each other, normalized to the count of elementary flux modes of the constituent networks. The count of elementary flux modes can be thought of as an estimate of the number of distinct metabolic tasks that the network can perform, i.e. its versatility. Hence, the graph shows how the versatility of two conjoined networks relative to the constituent networks is maximal for an intermediate Jaccard’s distance between such networks. 100 random paired networks were generated for each of several possible Jaccard’s distances. Bar heights reflect the average normalized increase in the number of elementary flux modes, whereas error bars represent the standard error of the mean.</p

Metabolic Proximity in the Order of Colonization of a Microbial Community

<div><p>Microbial biofilms are often composed of multiple bacterial species that accumulate by adhering to a surface and to each other. Biofilms can be resistant to antibiotics and physical stresses, posing unresolved challenges in the fight against infectious diseases. It has been suggested that early colonizers of certain biofilms could cause local environmental changes, favoring the aggregation of subsequent organisms. Here we ask whether the enzyme content of different microbes in a well-characterized dental biofilm can be used to predict their order of colonization. We define a metabolic distance between different species, based on the overlap in their enzyme content. We next use this metric to quantify the average metabolic distance between neighboring organisms in the biofilm. We find that this distance is significantly smaller than the one observed for a random choice of prokaryotes, probably reflecting the environmental constraints on metabolic function of the community. More surprisingly, this metabolic metric is able to discriminate between observed and randomized orders of colonization of the biofilm, with the observed orders displaying smaller metabolic distance than randomized ones. By complementing these results with the analysis of individual vs. joint metabolic networks, we find that the tendency towards minimal metabolic distance may be counter-balanced by a propensity to pair organisms with maximal joint potential for synergistic interactions. The trade-off between these two tendencies may create a “sweet spot” of optimal inter-organism distance, with possible broad implications for our understanding of microbial community organization.</p></div

Metabolic pathway enrichment across layers.

<p>Based on the enzyme content of the different species found in different layers of the biofilm (with layers labeled from 1 to 4, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g001" target="_blank">Fig. 1</a>), one can estimate whether any given layer is enriched for specific metabolic functions. Enzyme and pathway enrichments for each layer are computed based on a standard GSEA algorithm. Black boxes in the pathways-by-layers matrix denote enrichment of a particular KEGG pathway in a given layer. The pathway abbreviations are as follows: APM-Arginine and proline metabolism, BAOLN-Biosynthesis of alkaloids derived from ornithine lysine and nicotinic acid, CFP-Carbon fixation pathways in prokaryotes, GDM-Glyoxylate and dicarboxylate metabolism, GST-Glycine, serine and threonine metabolism, NM-Nitrogen metabolism, PCM-Porphyrin and chlorophyll metabolism, PGI-Pentose and glucuronate interconversions, PPM-Propanoate metabolism, PYM-Pyruvate metabolism, TBB-Terpenoid backbone biosynthesis, and TCA-Tricarboxylic acid cycle.</p

A simplified model of dental biofilm.

<p>(A) Rectangular nodes represent components of the salivary pellicle while circular nodes represent organisms in the biofilm. Lines represent known interactions (often mediated by adhesin molecules) between different components of the biofilm. The organism abbreviations are as follows: Layer 1: SM-<i>Streptococcus mitis</i>, SS-<i>Streptococcus sanguinis</i> and SG-<i>Streptococcus gordonii</i>. Layer 2: CO-<i>Capnocytophaga ochracea</i>,VS-<i>Veionella</i> (represented by <i>Veillonella parvula</i>), and PA-<i>Propionibacterium acnes</i>. Layer 3: FN-<i>Fusobacterium nucleatum</i>. Layer 4: AA-<i>Aggregatibacter actinomycetemcomitans</i>, TD-<i>Treponema denticola</i>, ES-<i>Eubacterium</i> (represented by <i>Eubacterium eligens</i>), and PG-<i>Porphyromonas gingivalis</i>. The salivary receptors have the following abbreviations: SMU-Sialylated mucins, BCF-Bacterial cell fragment, PRP-Proline rich protein, SA-Salivary agglutinin, S- Statherin and AAM-Alpha amylase. (B) A schematic representation of one of the many possible step-wise orders of colonization that conforms to the layered organization inferred from the literature, i.e. is such that the path that walks through the different species is monotonically departing from the salivary pellicle upwards. In our calculations of inter-species metabolic distances, we average the distances between any two species connected by a segment. This calculation is performed for all paths that reflect the order of colonization, giving rise to the distributions shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077617#pone-0077617-g002" target="_blank">Fig. 2</a>. (C) A schematic representation of one of the many possible randomized orders of colonization that do not follow the order of the literature-derived layers; for the 11 organisms present in the biofilm there are 11! possible permutations.</p

Biochemical Characterization of Hypothetical Proteins from <i>Helicobacter pylori</i>

<div><p>The functional characterization of Open Reading Frames (ORFs) from sequenced genomes remains a bottleneck in our effort to understand microbial biology. In particular, the functional characterization of proteins with only remote sequence homology to known proteins can be challenging, as there may be few clues to guide initial experiments. Affinity enrichment of proteins from cell lysates, and a global perspective of protein function as provided by COMBREX, affords an approach to this problem. We present here the biochemical analysis of six proteins from <i>Helicobacter pylori</i> ATCC 26695, a focus organism in COMBREX. Initial hypotheses were based upon affinity capture of proteins from total cellular lysate using derivatized nano-particles, and subsequent identification by mass spectrometry. Candidate genes encoding these proteins were cloned and expressed in <i>Escherichia coli</i>, and the recombinant proteins were purified and characterized biochemically and their biochemical parameters compared with the native ones. These proteins include a guanosine triphosphate (GTP) cyclohydrolase (HP0959), an ATPase (HP1079), an adenosine deaminase (HP0267), a phosphodiesterase (HP1042), an aminopeptidase (HP1037), and new substrates were characterized for a peptidoglycan deacetylase (HP0310). Generally, characterized enzymes were active at acidic to neutral pH (4.0–7.5) with temperature optima ranging from 35 to 55°C, although some exhibited outstanding characteristics.</p></div

Optimal temperature (upper) and pH (lower) for hypothetical proteins from <i>H.</i><i>pylori</i>.

<p>The heat map colors represent the relative percentages of activity (in terms of <i>k_cat</i>) as compared to the maximum (100%) within each enzyme. k_cat values were determined using nonlinear regression to fit the values for initial velocity and substrate concentration to the Michaelis-Menten equation as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066605#s2" target="_blank">Materials and Methods</a>. The pH dependence of a reaction was tested in the range of pH 4.0–9.5 at 30°C, and the temperature dependence in the range of 30–70°C at the optimal pH (4.0 for HP0142 and HP0959; 6.0 for HP0179 and HP0310; 7.5 for HP0267; and 4.5 for HP1037). The buffers used were: citrate (pH 4.0–5.0), acetate (pH 5.0–6.0), MES (pH 6.0–7.0), HEPES (pH 7.0–8.0), Tris-HCl (pH 8.0–9.0) and glycine (pH 9.0–9.5), all at 100 mM. Reaction conditions: [E]_o =  0–12 nM, [substrate: HP0310 (<i>p</i>NPacetate and acetylated xylan), HP0267 (adenosine), HP1037 (N-succinyl-Ala-Ala-Ala-Pro-Phe-<i>p</i>-nitroanilide), HP1079 (ATP), HP0959 (GTP), HP1042 (bis-<i>p</i>NPP)] ranging from 0 to 20 mM. Three independent experiments were performed for each parameter and graphs were plotted using mean values.</p

Genes tested in this paper.

<p>Genes tested in this paper.</p

Kinetic parameters of native pure proteins directly isolated from <i>Helicobacter pylori</i>.

a<p>Abbreviations: bis-<i>p</i>NPP, bis-<i>p</i>-nitrophenyl phosphate; NSAAAPPpNA, N-succinyl-Ala-Ala-Ala-Pro-Phe-<i>p</i>-nitroanilide; PGPApNA, pyroglutamyl-Pro-Arg-p-nitroanilide; SAAApNA, succinyl-Ala-Ala-Ala-<i>p</i>-nitroanilide; PAMCA, Phe-Arg-methylcoumarine amide; SLLVTMCA, succinyl-Leu-Leu-Val-Tyr-methylcoumarine amide.</p>b<p>Kinetic parameters determined at 30°C as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066605#pone-0066605-g001" target="_blank">Figure 1</a> legend and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066605#s2" target="_blank">Materials and Methods</a>.</p>c<p>Previously reported numbers for the kinetic parameters are obtained from Brenda <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066605#pone.0066605-Scheer1" target="_blank">[26]</a>, and listed in parentheses.</p

Extended impact of genes identified.

a<p>The number of proteins in COMBREX which have BLAST E values < 1×10⁻⁵, and alignment over 80% of both the query and target sequence.</p>b<p>The number of proteins contained in the protein families (from NCBI ProtClustDB <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066605#pone.0066605-Klimke1" target="_blank">[38]</a>) that have at least one BLAST hit, as defined above.</p>c<p>The number of proteins in the NCBI Non-Redundant database (NR) which have BLAST E values <1×10⁻⁵.</p