Erica Sodergren

Enteric defensins are essential regulators of intestinal microbial ecology.

Antimicrobial peptides are important effectors of innate immunity throughout the plant and animal kingdoms. In the mammalian small intestine, Paneth cell α-defensins are antimicrobial peptides that contribute to host defense against enteric pathogens. To determine if α-defensins also govern intestinal microbial ecology, we analyzed the intestinal microbiota of mice expressing a human α-defensin gene (DEFA5) and in mice lacking an enzyme required for the processing of mouse α-defensins. In these complementary models, we detected significant α-defensin-dependent changes in microbiota composition, but not in total bacterial numbers. Furthermore, DEFA5-expressing mice had striking losses of segmented filamentous bacteria and fewer interleukin 17 (IL-17)-producing lamina propria T cells. Our data ascribe a new homeostatic role to α-defensins in regulating the makeup of the commensal microbiota.

Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications

Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The environmental packages apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

Comparison of the Respiratory Microbiome in Healthy Nonsmokers and Smokers

RATIONALEnResults from 16S rDNA-encoding gene sequence-based, culture-independent techniques have led to conflicting conclusions about the composition of the lower respiratory tract microbiome.nnnOBJECTIVESnTo compare the microbiome of the upper and lower respiratory tract in healthy HIV-uninfected nonsmokers and smokers in a multicenter cohort.nnnMETHODSnParticipants were nonsmokers and smokers without significant comorbidities. Oral washes and bronchoscopic alveolar lavages were collected in a standardized manner. Sequence analysis of bacterial 16S rRNA-encoding genes was performed, and the neutral model in community ecology was used to identify bacteria that were the most plausible members of a lung microbiome.nnnMEASUREMENTS AND MAIN RESULTSnSixty-four participants were enrolled. Most bacteria identified in the lung were also in the mouth, but specific bacteria such as Enterobacteriaceae, Haemophilus, Methylobacterium, and Ralstonia species were disproportionally represented in the lungs compared with values predicted by the neutral model. Tropheryma was also in the lung, but not the mouth. Mouth communities differed between nonsmokers and smokers in species such as Porphyromonas, Neisseria, and Gemella, but lung bacterial populations did not.nnnCONCLUSIONSnThis study is the largest to examine composition of the lower respiratory tract microbiome in healthy individuals and the first to use the neutral model to compare the lung to the mouth. Specific bacteria appear in significantly higher abundance in the lungs than would be expected if they originated from the mouth, demonstrating that the lung microbiome does not derive entirely from the mouth. The mouth microbiome differs in nonsmokers and smokers, but lung communities were not significantly altered by smoking.

Propionibacterium acnes Strain Populations in the Human Skin Microbiome Associated with Acne

The human skin microbiome plays important roles in skin health and disease. However, bacterial population structure and diversity at the strain level is poorly understood. We compared the skin microbiome at the strain level and genome level of Propionibacterium acnes, a dominant skin commensal, between 49 acne patients and 52 healthy individuals by sampling the pilosebaceous units on their noses. Metagenomic analysis demonstrated that while the relative abundances of P. acnes were similar, the strain population structures were significantly different in the two cohorts. Certain strains were highly associated with acne and other strains were enriched in healthy skin. By sequencing 66 previously unreported P. acnes strains and comparing 71 P. acnes genomes, we identified potential genetic determinants of various P. acnes strains in association with acne or health. Our analysis suggests that acquired DNA sequences and bacterial immune elements may play roles in determining virulence properties of P. acnes strains and some could be future targets for therapeutic interventions. This study demonstrates a previously unreported paradigm of commensal strain populations that could explain the pathogenesis of human diseases. It underscores the importance of strain level analysis of the human microbiome to define the role of commensals in health and disease.

Genetic Basis for In Vivo Daptomycin Resistance in Enterococci

BACKGROUNDnDaptomycin is a lipopeptide with bactericidal activity that acts on the cell membrane of enterococci and is often used off-label to treat patients infected with vancomycin-resistant enterococci. However, the emergence of resistance to daptomycin during therapy threatens its usefulness.nnnMETHODSnWe performed whole-genome sequencing and characterization of the cell envelope of a clinical pair of vancomycin-resistant Enterococcus faecalis isolates from the blood of a patient with fatal bacteremia; one isolate (S613) was from blood drawn before treatment and the other isolate (R712) was from blood drawn after treatment with daptomycin. The minimal inhibitory concentrations (MICs) of these two isolates were 1 and 12 μg per milliliter, respectively. Gene replacements were made to exchange the alleles found in isolate S613 with those in isolate R712.nnnRESULTSnIsolate R712 had in-frame deletions in three genes. Two genes encoded putative enzymes involved in phospholipid metabolism, GdpD (which denotes glycerophosphoryl diester phosphodiesterase) and Cls (which denotes cardiolipin synthetase), and one gene encoded a putative membrane protein, LiaF (which denotes lipid II cycle-interfering antibiotics protein but whose exact function is not known). LiaF is predicted to be a member of a three-component regulatory system (LiaFSR) involved in the stress-sensing response of the cell envelope to antibiotics. Replacement of the liaF allele of isolate S613 with the liaF allele from isolate R712 quadrupled the MIC of daptomycin, whereas replacement of the gdpD allele had no effect on MIC. Replacement of both the liaF and gdpD alleles of isolate S613 with the liaF and gdpD alleles of isolate R712 raised the daptomycin MIC for isolate S613 to 12 μg per milliliter. As compared with isolate S613, isolate R712--the daptomycin-resistant isolate--had changes in the structure of the cell envelope and alterations in membrane permeability and membrane potential.nnnCONCLUSIONSnMutations in genes encoding LiaF and a GdpD-family protein were necessary and sufficient for the development of resistance to daptomycin during the treatment of vancomycin-resistant enterococci. (Funded by the National Institute of Allergy and Infectious Diseases and the National Institutes of Health.).

Complete Genome Sequence of Rickettsia typhi and Comparison with Sequences of Other Rickettsiae

Rickettsia typhi, the causative agent of murine typhus, is an obligate intracellular bacterium with a life cycle involving both vertebrate and invertebrate hosts. Here we present the complete genome sequence of R. typhi (1,111,496 bp) and compare it to the two published rickettsial genome sequences: R. prowazekii and R. conorii. We identified 877 genes in R. typhi encoding 3 rRNAs, 33 tRNAs, 3 noncoding RNAs, and 838 proteins, 3 of which are frameshifts. In addition, we discovered more than 40 pseudogenes, including the entire cytochrome c oxidase system. The three rickettsial genomes share 775 genes: 23 are found only in R. prowazekii and R. typhi, 15 are found only in R. conorii and R. typhi, and 24 are unique to R. typhi. Although most of the genes are colinear, there is a 35-kb inversion in gene order, which is close to the replication terminus, in R. typhi, compared to R. prowazekii and R. conorii. In addition, we found a 124-kb R. typhi-specific inversion, starting 19 kb from the origin of replication, compared to R. prowazekii and R. conorii. Inversions in this region are also seen in the unpublished genome sequences of R. sibirica and R. rickettsii, indicating that this region is a hot spot for rearrangements. Genome comparisons also revealed a 12-kb insertion in the R. prowazekii genome, relative to R. typhi and R. conorii, which appears to have occurred after the typhus (R. prowazekii and R. typhi) and spotted fever (R. conorii) groups diverged. The three-way comparison allowed further in silico analysis of the SpoT split genes, leading us to propose that the stringent response system is still functional in these rickettsiae.

Evaluation of 16s rDNA-based community profiling for human microbiome research

The Human Microbiome Project will establish a reference data set for analysis of the microbiome of healthy adults by surveying multiple body sites from 300 people and generating data from over 12,000 samples. To characterize these samples, the participating sequencing centers evaluated and adopted 16S rDNA community profiling protocols for ABI 3730 and 454 FLX Titanium sequencing. In the course of establishing protocols, we examined the performance and error characteristics of each technology, and the relationship of sequence error to the utility of 16S rDNA regions for classification- and OTU-based analysis of community structure. The data production protocols used for this work are those used by the participating centers to produce 16S rDNA sequence for the Human Microbiome Project. Thus, these results can be informative for interpreting the large body of clinical 16S rDNA data produced for this project.

Patterned progression of bacterial populations in the premature infant gut

Significance It is increasingly apparent that bacteria in the gut are important determinants of health and disease in humans. However, we know remarkably little about how this organ transitions from a sterile/near-sterile state at birth to one that soon harbors a highly diverse biomass. We show in premature infants a patterned progression of the gut bacterial community that is only minimally influenced by mode of delivery, antibiotics, or feeds. The pace of this progression is most strongly influenced by gestational age, with the microbial population assembling slowest for infants born most prematurely. These data raise the possibility that host biology, more than exogenous factors such as antibiotics, feeds, and route of delivery, drives bacterial populations in the premature newborn infant gut. In the weeks after birth, the gut acquires a nascent microbiome, and starts its transition to bacterial population equilibrium. This early-in-life microbial population quite likely influences later-in-life host biology. However, we know little about the governance of community development: does the gut serve as a passive incubator where the first organisms randomly encountered gain entry and predominate, or is there an orderly progression of members joining the community of bacteria? We used fine interval enumeration of microbes in stools from multiple subjects to answer this question. We demonstrate via 16S rRNA gene pyrosequencing of 922 specimens from 58 subjects that the gut microbiota of premature infants residing in a tightly controlled microbial environment progresses through a choreographed succession of bacterial classes from Bacilli to Gammaproteobacteria to Clostridia, interrupted by abrupt population changes. As infants approach 33–36 wk postconceptional age (corresponding to the third to the twelfth weeks of life depending on gestational age at birth), the gut is well colonized by anaerobes. Antibiotics, vaginal vs. Caesarian birth, diet, and age of the infants when sampled influence the pace, but not the sequence, of progression. Our results suggest that in infants in a microbiologically constrained ecosphere of a neonatal intensive care unit, gut bacterial communities have an overall nonrandom assembly that is punctuated by microbial population abruptions. The possibility that the pace of this assembly depends more on host biology (chiefly gestational age at birth) than identifiable exogenous factors warrants further consideration.

Biogeography of the ecosystems of the healthy human body

BackgroundCharacterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans.ResultsWe identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies.ConclusionsThe data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing.

Inflammatory Bowel Diseases Phenotype, C. difficile and NOD2 Genotype Are Associated with Shifts in Human Ileum Associated Microbial Composition

We tested the hypothesis that Crohn’s disease (CD)-related genetic polymorphisms involved in host innate immunity are associated with shifts in human ileum–associated microbial composition in a cross-sectional analysis of human ileal samples. Sanger sequencing of the bacterial 16S ribosomal RNA (rRNA) gene and 454 sequencing of 16S rRNA gene hypervariable regions (V1–V3 and V3–V5), were conducted on macroscopically disease-unaffected ileal biopsies collected from 52 ileal CD, 58 ulcerative colitis and 60 control patients without inflammatory bowel diseases (IBD) undergoing initial surgical resection. These subjects also were genotyped for the three major NOD2 risk alleles (Leu1007fs, R708W, G908R) and the ATG16L1 risk allele (T300A). The samples were linked to clinical metadata, including body mass index, smoking status and Clostridia difficile infection. The sequences were classified into seven phyla/subphyla categories using the Naïve Bayesian Classifier of the Ribosome Database Project. Centered log ratio transformation of six predominant categories was included as the dependent variable in the permutation based MANCOVA for the overall composition with stepwise variable selection. Polymerase chain reaction (PCR) assays were conducted to measure the relative frequencies of the Clostridium coccoides – Eubacterium rectales group and the Faecalibacterium prausnitzii spp. Empiric logit transformations of the relative frequencies of these two microbial groups were included in permutation-based ANCOVA. Regardless of sequencing method, IBD phenotype, Clostridia difficile and NOD2 genotype were selected as associated (FDR ≤0.05) with shifts in overall microbial composition. IBD phenotype and NOD2 genotype were also selected as associated with shifts in the relative frequency of the C. coccoides – E. rectales group. IBD phenotype, smoking and IBD medications were selected as associated with shifts in the relative frequency of F. prausnitzii spp. These results indicate that the effects of genetic and environmental factors on IBD are mediated at least in part by the enteric microbiota.