Stool microbiome reveals diverse bacterial ureases as confounders of oral urea breath testing for Helicobacter pylori and Mycobacterium tuberculosis in Bamako, Mali.

Detection of bacterial urease activity has been utilized successfully to diagnose Helicobacter pylori (H. pylori). While Mycobacterium tuberculosis (M. tuberculosis) also possesses an active urease, it is unknown whether detection of mycobacterial urease activity by oral urease breath test (UBT) can be exploited as a rapid point of care biomarker for tuberculosis (TB) in humans. We enrolled 34 individuals newly diagnosed with pulmonary TB and 46 healthy subjects in Bamako, Mali and performed oral UBT, mycobacterial sputum culture and H. pylori testing. Oral UBT had a sensitivity and specificity (95% CI) of 70% (46-88%) and 11% (3-26%), respectively, to diagnose culture-confirmed M. tuberculosis disease among patients without H. pylori, and 100% sensitivity (69-100%) and 11% specificity (3-26%) to diagnose H. pylori among patients without pulmonary TB. Stool microbiome analysis of controls without TB or H. pylori but with positive oral UBT detected high levels of non-H. pylori urease producing organisms, which likely explains the low specificity of oral UBT in this setting and in other reports of oral UBT studies in Africa.

Metabolic proximity in the order of colonization of a microbial community.

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.

Emergence of epidemic multidrug-resistant Enterococcus faecium from animal and commensal strains.

UNLABELLED: Enterococcus faecium, natively a gut commensal organism, emerged as a leading cause of multidrug-resistant hospital-acquired infection in the 1980s. As the living record of its adaptation to changes in habitat, we sequenced the genomes of 51 strains, isolated from various ecological environments, to understand how E. faecium emerged as a leading hospital pathogen. Because of the scale and diversity of the sampled strains, we were able to resolve the lineage responsible for epidemic, multidrug-resistant human infection from other strains and to measure the evolutionary distances between groups. We found that the epidemic hospital-adapted lineage is rapidly evolving and emerged approximately 75 years ago, concomitant with the introduction of antibiotics, from a population that included the majority of animal strains, and not from human commensal lines. We further found that the lineage that included most strains of animal origin diverged from the main human commensal line approximately 3,000 years ago, a time that corresponds to increasing urbanization of humans, development of hygienic practices, and domestication of animals, which we speculate contributed to their ecological separation. Each bifurcation was accompanied by the acquisition of new metabolic capabilities and colonization traits on mobile elements and the loss of function and genome remodeling associated with mobile element insertion and movement. As a result, diversity within the species, in terms of sequence divergence as well as gene content, spans a range usually associated with speciation. IMPORTANCE: Enterococci, in particular vancomycin-resistant Enterococcus faecium, recently emerged as a leading cause of hospital-acquired infection worldwide. In this study, we examined genome sequence data to understand the bacterial adaptations that accompanied this transformation from microbes that existed for eons as members of host microbiota. We observed changes in the genomes that paralleled changes in human behavior. An initial bifurcation within the species appears to have occurred at a time that corresponds to the urbanization of humans and domestication of animals, and a more recent bifurcation parallels the introduction of antibiotics in medicine and agriculture. In response to the opportunity to fill niches associated with changes in human activity, a rapidly evolving lineage emerged, a lineage responsible for the vast majority of multidrug-resistant E. faecium infections.

Biochemical Characterization of Hypothetical Proteins from Helicobacter pylori.

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 Helicobacter pylori 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 Escherichia coli, 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.

Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

COMBREX: a project to accelerate the functional annotation of prokaryotic genomes.

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

Metabolic network model of a human oral pathogen.

The microbial community present in the human mouth is engaged in a complex network of diverse metabolic activities. In addition to serving as energy and building-block sources, metabolites are key players in interspecies and host-pathogen interactions. Metabolites are also implicated in triggering the local inflammatory response, which can affect systemic conditions such as atherosclerosis, obesity, and diabetes. While the genome of several oral pathogens has been sequenced, quantitative understanding of the metabolic functions of any oral pathogen at the system level has not been explored yet. Here we pursue the computational construction and analysis of the genome-scale metabolic network of Porphyromonas gingivalis, a gram-negative anaerobe that is endemic in the human population and largely responsible for adult periodontitis. Integrating information from the genome, online databases, and literature screening, we built a stoichiometric model that encompasses 679 metabolic reactions. By using flux balance approaches and automated network visualization, we analyze the growth capacity under amino-acid-rich medium and provide evidence that amino acid preference and cytotoxic by-product secretion rates are suitably reproduced by the model. To provide further insight into the basic metabolic functions of P. gingivalis and suggest potential drug targets, we study systematically how the network responds to any reaction knockout. We focus specifically on the lipopolysaccharide biosynthesis pathway and identify eight putative targets, one of which has been recently verified experimentally. The current model, which is amenable to further experimental testing and refinements, could prove useful in evaluating the oral microbiome dynamics and in the development of novel biomedical applications.