It is now apparent that the complex microbial communities found on and in the human body vary across individuals. What has largely been missing from previous studies is an understanding of how these communities vary over time within individuals. To the extent to which it has been considered, it is often assumed that temporal variability is negligible for healthy adults. Here we address this gap in understanding by profiling the forehead, gut (fecal), palm, and tongue microbial communities in 85 adults, weekly over 3 months.
We found that skin (forehead and palm) varied most in the number of taxa present, whereas gut and tongue communities varied more in the relative abundances of taxa. Within each body habitat, there was a wide range of temporal variability across the study population, with some individuals harboring more variable communities than others. The best predictor of these differences in variability across individuals was microbial diversity; individuals with more diverse gut or tongue communities were more stable in composition than individuals with less diverse communities.
Longitudinal sampling of a relatively large number of individuals allowed us to observe high levels of temporal variability in both diversity and community structure in all body habitats studied. These findings suggest that temporal dynamics may need to be considered when attempting to link changes in microbiome structure to changes in health status. Furthermore, our findings show that, not only is the composition of an individual’s microbiome highly personalized, but their degree of temporal variability is also a personalized feature.
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The online version of this article (doi:10.1186/s13059-014-0531-y) contains supplementary material, which is available to authorized users.
Approximately 100 loci have been definitively associated with rheumatoid arthritis (RA) susceptibility. However, they explain only a fraction of RA heritability. Interactions between polymorphisms could explain part of the remaining heritability. Multiple interactions have been reported, but only the shared epitope (SE) × protein tyrosine phosphatase nonreceptor type 22 (PTPN22) interaction has been replicated convincingly. Two recent studies deserve attention because of their quality, including their replication in a second sample collection. In one of them, researchers identified interactions between PTPN22 and seven single-nucleotide polymorphisms (SNPs). The other showed interactions between the SE and the null genotype of glutathione S-transferase Mu 1 (GSTM1) in the anti–cyclic citrullinated peptide–positive (anti-CCP+) patients. In the present study, we aimed to replicate association with RA susceptibility of interactions described in these two high-quality studies.
A total of 1,744 patients with RA and 1,650 healthy controls of Spanish ancestry were studied. Polymorphisms were genotyped by single-base extension. SE genotypes of 736 patients were available from previous studies. Interaction analysis was done using multiple methods, including those originally reported and the most powerful methods described.
Genotypes of one of the SNPs (rs4695888) failed quality control tests. The call rate for the other eight polymorphisms was 99.9%. The frequencies of the polymorphisms were similar in RA patients and controls, except for PTPN22 SNP. None of the interactions between PTPN22 SNPs and the six SNPs that met quality control tests was replicated as a significant interaction term—the originally reported finding—or with any of the other methods. Nor was the interaction between GSTM1 and the SE replicated as a departure from additivity in anti-CCP+ patients or with any of the other methods.
None of the interactions tested were replicated in spite of sufficient power and assessment with different assays. These negative results indicate that whether interactions are significant contributors to RA susceptibility remains unknown and that strict standards need to be applied to claim that an interaction exists.
We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.
OTU picking; Microbial ecology; Microbiome; Qiime; Bioinformatics
Ticks are important vectors for many emerging pathogens. However, they are also infected with many symbionts and commensals, often competing for the same niches. In this paper, we characterize the microbiome of Amblyomma americanum (Acari: Ixodidae), the lone star tick, in order to better understand the evolutionary relationships between pathogens and nonpathogens. Multitag pyrosequencing of prokaryotic 16S rRNA genes (16S rRNA) was performed on 20 lone star ticks (including males, females, and nymphs). Pyrosequencing of the rickettsial sca0 gene (also known as ompA or rompA) was performed on six ticks. Female ticks had less diverse microbiomes than males and nymphs, with greater population densities of Rickettsiales. The most common members of Rickettsiales were “Candidatus Rickettsia amblyommii” and “Candidatus Midichloria mitochondrii.” “Ca. Rickettsia amblyommii” was 2.6-fold more common in females than males, and there was no sequence diversity in the sca0 gene. These results are consistent with a predominantly vertical transmission pattern for “Ca. Rickettsia amblyommii.”
The Deepwater Horizon (DWH) oil spill in the spring of 2010 resulted in an input of ∼4.1 million barrels of oil to the Gulf of Mexico; >22% of this oil is unaccounted for, with unknown environmental consequences. Here we investigated the impact of oil deposition on microbial communities in surface sediments collected at 64 sites by targeted sequencing of 16S rRNA genes, shotgun metagenomic sequencing of 14 of these samples and mineralization experiments using 14C-labeled model substrates. The 16S rRNA gene data indicated that the most heavily oil-impacted sediments were enriched in an uncultured Gammaproteobacterium and a Colwellia species, both of which were highly similar to sequences in the DWH deep-sea hydrocarbon plume. The primary drivers in structuring the microbial community were nitrogen and hydrocarbons. Annotation of unassembled metagenomic data revealed the most abundant hydrocarbon degradation pathway encoded genes involved in degrading aliphatic and simple aromatics via butane monooxygenase. The activity of key hydrocarbon degradation pathways by sediment microbes was confirmed by determining the mineralization of 14C-labeled model substrates in the following order: propylene glycol, dodecane, toluene and phenanthrene. Further, analysis of metagenomic sequence data revealed an increase in abundance of genes involved in denitrification pathways in samples that exceeded the Environmental Protection Agency (EPA)'s benchmarks for polycyclic aromatic hydrocarbons (PAHs) compared with those that did not. Importantly, these data demonstrate that the indigenous sediment microbiota contributed an important ecosystem service for remediation of oil in the Gulf. However, PAHs were more recalcitrant to degradation, and their persistence could have deleterious impacts on the sediment ecosystem.
DWH oil spill; hydrocarbons; iTag/Metagenomics; microbial community structure; sediments
We aimed to replicate a recent study which showed higher genetic risk load at 15 loci in men than in women with systemic lupus erythematosus (SLE). This difference was very significant, and it was interpreted as indicating that men require more genetic susceptibility than women to develop SLE.
Nineteen SLE-associated loci (thirteen of which are shared with the previous study) were analyzed in 1,457 SLE patients and 1,728 healthy controls of European ancestry. Genetic risk load was calculated as sex-specific sum genetic risk scores (GRSs).
Our results did not replicate those of the previous study at either the level of individual loci or the global level of GRSs. GRSs were larger in women than in men (4.20 ± 1.07 in women vs. 3.27 ± 0.98 in men). This very significant difference (P < 10−16) was more dependent on the six new loci not included in the previous study (59% of the difference) than on the thirteen loci that are shared (the remaining 41%). However, the 13 shared loci also showed a higher genetic risk load in women than in men in our study (P = 6.6 × 10−7), suggesting that heterogeneity of participants, in addition to different loci, contributed to the opposite results.
Our results show the lack of a clear trend toward higher genetic risk in one of the sexes for the analyzed SLE loci. They also highlight several limitations of assessments of genetic risk load, including the possibility of ascertainment bias with loci discovered in studies that have included mainly women.
Some association studies, as the implemented in VEGAS, ALIGATOR, i-GSEA4GWAS, GSA-SNP and other software tools, use genes as the unit of analysis. These genes include the coding sequence plus flanking sequences. Polymorphisms in the flanking sequences are of interest because they involve cis-regulatory elements or they inform on untyped genetic variants trough linkage disequilibrium. Gene extensions have customarily been defined as ± 50 Kb. This approach is not fully satisfactory because genetic relationships between neighbouring sequences are a function of genetic distances, which are only poorly replaced by physical distances.
Standardized recombination rates (SRR) from the deCODE recombination map were used as units of genetic distances. We searched for a SRR producing flanking sequences near the ± 50 Kb offset that has been common in previous studies. A SRR ≥ 2 was selected because it led to gene extensions with median length = 45.3 Kb and the simplicity of an integer value. As expected, boundaries of the genes defined with the ± 50 Kb and with the SRR ≥2 rules were rarely concordant. The impact of these differences was illustrated with the interpretation of top association signals from two large studies including many hits and their detailed analysis based in different criteria. The definition based in genetic distance was more concordant with the results of these studies than the based in physical distance. In the analysis of 18 top disease associated loci form the first study, the SRR ≥2 genes led to a fully concordant interpretation in 17 loci; the ± 50 Kb genes only in 6. Interpretation of the 43 putative functional genes of the second study based in the SRR ≥2 definition only missed 4 of the genes, whereas the based in the ± 50 Kb definition missed 10 genes.
A gene definition based on genetic distance led to results more concordant with expert detailed analyses than the commonly used based in physical distance. The genome coordinates for each gene are provided to maintain a simple use of the new definitions.
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Suppressor of cytokine signalling (SOCS) proteins are inhibitors of cytokine signalling that function via the JAK/STAT pathway (Janus kinase/signal transducers and activators of transcription). Eight SOCS proteins, SOCS1–SOCS7 and CIS-1 (cytokine-inducible SH2-domain, with similar structure to the other SOCS proteins) have been identified, of which SOCS1, 2, and 3 and CIS-1 are the best characterised. A characteristic feature of osteoarthritis (OA) is increased production by articular chondrocytes of proinflammatory cytokines, such as interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNFα), which may be induced by mechanotransduction and contribute to cartilage destruction. In this study, we have compared the gene expression of SOCS1, 2, 3 and CIS-1 in healthy and OA human chondrocytes, and also analyzed the effects of IL-1β and TNFα on the levels of mRNA encoding these SOCS family members. In addition, SOCS2 protein production was assessed and the CpG methylation status of the SOCS2 promoter was analyzed to determine the role of epigenetics in its regulation.
Femoral heads were obtained after joint replacement surgery for late stage OA and hemiarthroplasty following a fracture of the neck of femur (#NOF). Chondrocytes from the superficial layer of OA cartilage and the deep zone of #NOF cartilage were isolated by sequential treatment with trypsin, hyaluronidase and collagenase B. Total DNA and RNA were extracted from the same chondrocytes, and the levels of SOCS1, 2, 3 and CIS-1 mRNA were determined by qRT-PCR. The percentage of methylation in the CpG sites of the SOCS2 proximal promoter was quantified by pyrosequencing. Alternatively, healthy chondrocytes were isolated from #NOF cartilage and cultured with and without a mixture of IL-1β and oncostatin M (OSM, both 2.5 ng/ml) or TNFα (10 ng/ml). The short-term cultures with single cytokine treatment were harvested 24 and 72 h after treatment, and the long-term cultures were maintained for 4–5 weeks until confluent with periodical cytokine stimulation. Total RNA was extracted and mRNA levels were determined by qRT-PCR.
The SOCS2 and CIS-1 mRNA levels were reduced by approximately 10-fold in OA samples compared to control samples, while SOCS1 and SOCS3 showed similar expression patterns in OA and control chondrocytes. The SOCS2 and CIS-1 mRNA levels declined by 6-fold and 3-fold with long-term treatment with IL-1β and OSM in combination and TNFα, respectively. There was no significant difference in the CpG methylation status of the SOCS2 promoter between healthy and OA chondrocytes. Similarly, cytokine stimulation did not change the CpG methylation status of the SOCS2 promoter.
This study demonstrates the reduced expression of SOCS2 and CIS-1 in OA, while SOCS1 and SOCS3 were unaffected. The observation that long-term treatment with inflammatory cytokines attenuated the expression of SOCS2 and CIS-1 suggests a potential positive feedback mechanism, and a role of SOCS in the pathology of OA.
Osteoarthritis (OA); Chondrocytes; Suppressors of cytokine signalling (SOCS); Cytokine-inducible SH2 protein (CIS-1); IL-1β; TNFα
To investigate whether the abnormal expression of inducible nitric oxide synthase (iNOS) by osteoarthritic (OA) human chondrocytes is associated with changes in the DNA methylation status in the promoter and/or enhancer elements of iNOS.
Expression of iNOS was quantified by quantitative reverse transcriptase–polymerase chain reaction. The DNA methylation status of the iNOS promoter and enhancer regions was determined by bisulfite sequencing or pyrosequencing. The effect of CpG methylation on iNOS promoter and enhancer activities was determined using a CpG-free luciferase vector and a CpG methyltransferase. Cotransfections with expression vectors encoding NF-κB subunits were carried out to analyze iNOS promoter and enhancer activities in response to changes in methylation status.
The 1,000-bp iNOS promoter has only 7 CpG sites, 6 of which were highly methylated in both control and OA samples. The CpG site at −289 and the sites in the starting coding region were largely unmethylated in both groups. The NF-κB enhancer region at −5.8 kb was significantly demethylated in OA samples compared with control samples. This enhancer element was transactivated by cotransfection with the NF-κB subunit p65, alone or together with p50. Critically, methylation treatment of the iNOS enhancer element significantly decreased its activity in a reporter assay.
These findings demonstrate the association between demethylation of specific NF-κB–responsive enhancer elements and the activation of iNOS transactivation in human OA chondrocytes, consistent with the differences in methylation status observed in vivo in normal and human OA cartilage and, importantly, show association with the OA process.
As microbial ecologists take advantage of high-throughput sequencing technologies to describe microbial communities across ever-increasing numbers of samples, new analysis tools are required to relate the distribution of microbes among larger numbers of communities, and to use increasingly rich and standards-compliant metadata to understand the biological factors driving these relationships. In particular, the Earth Microbiome Project drives these needs by profiling the genomic content of tens of thousands of samples across multiple environment types.
Features of EMPeror include: ability to visualize gradients and categorical data, visualize different principal coordinates axes, present the data in the form of parallel coordinates, show taxa as well as environmental samples, dynamically adjust the size and transparency of the spheres representing the communities on a per-category basis, dynamically scale the axes according to the fraction of variance each explains, show, hide or recolor points according to arbitrary metadata including that compliant with the MIxS family of standards developed by the Genomic Standards Consortium, display jackknifed-resampled data to assess statistical confidence in clustering, perform coordinate comparisons (useful for procrustes analysis plots), and greatly reduce loading times and overall memory footprint compared with existing approaches. Additionally, ease of sharing, given EMPeror’s small output file size, enables agile collaboration by allowing users to embed these visualizations via emails or web pages without the need for extra plugins.
Here we present EMPeror, an open source and web browser enabled tool with a versatile command line interface that allows researchers to perform rapid exploratory investigations of 3D visualizations of microbial community data, such as the widely used principal coordinates plots. EMPeror includes a rich set of controllers to modify features as a function of the metadata. By being specifically tailored to the requirements of microbial ecologists, EMPeror thus increases the speed with which insight can be gained from large microbiome datasets.
Microbial ecology; QIIME; Data visualization
Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days. Our results provide a detailed understanding of bacterial and microbial eukaryotic ecology within a decomposing corpse system and suggest that microbial community data can be developed into a forensic tool for estimating PMI.
Our bodies—especially our skin, our saliva, the lining of our mouth and our gastrointestinal tract—are home to a diverse collection of bacteria and other microorganisms called the microbiome. While the roles played by many of these microorganisms have yet to be identified, it is known that they contribute to the health and wellbeing of their host by metabolizing indigestible compounds, producing essential vitamins, and preventing the growth of harmful bacteria. They are important for nutrient and carbon cycling in the environment.
The advent of advanced sequencing techniques has made it feasible to study the composition of this microbial community, and to monitor how it changes over time or how it responds to events such as antibiotic treatment. Sequencing studies have been used to highlight the significant differences between microbial communities found in different parts of the body, and to follow the evolution of the gut microbiome from birth. Most of these studies have focused on live animals, so little is known about what happens to the microbiome after its host dies. In particular, it is not known if the changes that occur after death are similar for all individuals. Moreover, the decomposing animal supplies nutrients and carbon to the surrounding ecosystem, but its influence on the microbial community of its immediate environment is not well understood.
Now Metcalf et al. have used high-throughput sequencing to study the bacteria and other microorganisms (such as nematodes and fungi) in dead and decomposing mice, and also in the soil beneath them, over the course of 48 days. The changes were significant and also consistent across the corpses, with the microbial communities in the corpses influencing those in the soil, and vice versa. Metcalf et al. also showed that these measurements could be used to estimate the postmortem interval (the time since death) to within approximately 3 days, which suggests that the work could have applications in forensic science.
decomposition; microbial succession; time since death; forensics; postmortem interval; microbial ecology; Mouse
The ability to detect a specific organism from a complex environment is vitally important to many fields of public health, including food safety. For example, tomatoes have been implicated numerous times as vehicles of foodborne outbreaks due to strains of Salmonella but few studies have ever recovered Salmonella from a tomato phyllosphere environment. Precision of culturing techniques that target agents associated with outbreaks depend on numerous factors. One important factor to better understand is which species co-enrich during enrichment procedures and how microbial dynamics may impede or enhance detection of target pathogens. We used a shotgun sequence approach to describe taxa associated with samples pre-enrichment and throughout the enrichment steps of the Bacteriological Analytical Manual's (BAM) protocol for detection of Salmonella from environmental tomato samples. Recent work has shown that during efforts to enrich Salmonella (Proteobacteria) from tomato field samples, Firmicute genera are also co-enriched and at least one co-enriching Firmicute genus (Paenibacillus sp.) can inhibit and even kills strains of Salmonella. Here we provide a baseline description of microflora that co-culture during detection efforts and the utility of a bioinformatic approach to detect specific taxa from metagenomic sequence data. We observed that uncultured samples clustered together with distinct taxonomic profiles relative to the three cultured treatments (Universal Pre-enrichment broth (UPB), Tetrathionate (TT), and Rappaport-Vassiliadis (RV)). There was little consistency among samples exposed to the same culturing medias, suggesting significant microbial differences in starting matrices or stochasticity associated with enrichment processes. Interestingly, Paenibacillus sp. (Salmonella inhibitor) was significantly enriched from uncultured to cultured (UPB) samples. Also of interest was the sequence based identification of a number of sequences as Salmonella despite indication by all media, that samples were culture negative for Salmonella. Our results substantiate the nascent utility of metagenomic methods to improve both biological and bioinformatic pathogen detection methods.
Ileocecal resection (ICR) is a commonly required surgical intervention in unmanageable Crohn’s disease and necrotizing enterocolitis. However, the impact of ICR, and the concomitant doses of antibiotic routinely given with ICR, on the intestinal commensal microbiota has not been determined. In this study, wild-type C57BL6 mice were subjected to ICR and concomitant single intraperitoneal antibiotic injection. Intestinal lumen contents were collected from jejunum and colon at 7, 14, and 28 days after resection and compared to non-ICR controls. Samples were analyzed by16S rRNA gene pyrosequencing and quantitative PCR. The intestinal microbiota was altered by 7 days after ICR and accompanying antibiotic treatment, with decreased diversity in the colon. Phylogenetic diversity (PD) decreased from 11.8 ± 1.8 in non-ICR controls to 5.9 ± 0.5 in 7-day post-ICR samples. There were also minor effects in the jejunum where PD values decreased from 8.3 ± 0.4 to 7.5 ± 1.4. PCoA analysis indicated that bacterial populations 28 days post-ICR differed significantly from non-ICR controls. Moreover, colon and jejunum bacterial populations were remarkably similar 28 days after resection, whereas the initial communities differed markedly. Firmicutes and Bacteroidetes were the predominant phyla in jejunum and colon before ICR; however, Firmicutes became the vastly predominant phylum in jejunum and colon 28 days after ICR. Although the microbiota returned towards a homeostatic state, with re-establishment of Firmicutes as the predominant phylum, we did not detect Bacteroidetes in the colon 28 days after ICR. In the jejunum Bacteroidetes was detected at a 0.01% abundance after this time period. The changes in jejunal and colonic microbiota induced by ICR and concomitant antibiotic injection may therefore be considered as potential regulators of post-surgical adaptive growth or function, and in a setting of active IBD, potential contributors to post-surgical pathophysiology of disease recurrence.
Polymorphisms in the interferon regulatory factor 5 (IRF5) gene are associated with susceptibility to systemic lupus erythematosus, rheumatoid arthritis and other diseases through independent risk and protective haplotypes. Several functional polymorphisms are already known, but they do not account for the protective haplotypes that are tagged by the minor allele of rs729302.
Polymorphisms in linkage disequilibrium (LD) with rs729302 or particularly associated with IRF5 expression were selected for functional screening, which involved electrophoretic mobility shift assays (EMSAs) and reporter gene assays.
A total of 54 single-nucleotide polymorphisms in the 5' region of IRF5 were genotyped. Twenty-four of them were selected for functional screening because of their high LD with rs729302 or protective haplotypes. In addition, two polymorphisms were selected for their prominent association with IRF5 expression. Seven of these twenty-six polymorphisms showed reproducible allele differences in EMSA. The seven were subsequently analyzed in gene reporter assays, and three of them showed significant differences between their two alleles: rs729302, rs13245639 and rs11269962. Haplotypes including the cis-regulatory polymorphisms correlated very well with IRF5 mRNA expression in an analysis based on previous data.
We have found that three polymorphisms in LD with the protective haplotypes of IRF5 have differential allele effects in EMSA and in reporter gene assays. Identification of these cis-regulatory polymorphisms will allow more accurate analysis of transcriptional regulation of IRF5 expression, more powerful genetic association studies and deeper insight into the role of IRF5 in disease susceptibility.
The objective of this study is to present an efficiency-perception impact assessment based upon the integration of fuzzy logic (FL) of the “Productive Reconversion” conservation program (PRP) instituted by the Mexican government, in the upper Gulf of California and the Colorado Delta Biosphere Reserve. This approach enables environmental analysts to deal with the intrinsic imprecision and ambiguity associated with people’s judgments and conclusions. The application of FL to the assessment of program efficiency is illustrated in this work, demonstrating how subjective perceptions can be converted into quantitative values easy to evaluate during the decision-making process.
Electronic supplementary material
The online version of this article (doi:10.1007/s13280-012-0252-y) contains supplementary material, which is available to authorized users.
Fuzzy logic; Marine protected area; Biosphere reserve; Human perception assessment; Gulf of California
Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in the elderly. It is characterized by changes in joint structure including degeneration of the articular cartilage and its etiology is multifactorial with a strong postulated genetic component. We performed a meta-analysis of four genome-wide association (GWA) studies of 2,371 knee OA cases and 35,909 controls in Caucasian populations. Replication of the top hits was attempted with data from additional ten replication datasets. With a cumulative sample size of 6,709 cases and 44,439 controls, we identified one genome-wide significant locus on chromosome 7q22 for knee OA (rs4730250, p-value=9.2×10−9), thereby confirming its role as a susceptibility locus for OA. The associated signal is located within a large (500kb) linkage disequilibrium (LD) block that contains six genes; PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, beta), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like), and BCAP29 (the B-cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.
HLA-B27 has a modifier effect on the phenotype of multiple diseases, both associated and non-associated with it. Among these effects, an increased frequency of clinical enthesitis in patients with Rheumatoid Arthritis (RA) has been reported but never explored again. We aimed to replicate this study with a sensitive and quantitative assessment of enthesitis by using standardized ultrasonography (US).
The Madrid Sonography Enthesitis Index (MASEI) was applied to the US assessment of 41 HLA-B27 positive and 41 matched HLA-B27 negative patients with longstanding RA. Clinical characteristics including explorations aimed to evaluate spondyloarthrtitis and laboratory tests were also done.
A significant degree of abnormalities in the entheses of the patients with RA were found, but the MASEI values, and each of its components including the Doppler signal, were similar in HLA-B27 positive and negative patients. An increase of the MASEI scores with age was identified. Differences in two clinical features were found: a lower prevalence of rheumatoid factor and a more common story of low back pain in the HLA-B27 positive patients than in the negative. The latter was accompanied by radiographic sacroiliitis in two HLA-B27 positive patients. No other differences were detected.
We have found that HLA-B27 positive patients with RA do not have more enthesitis as assessed with US than the patients lacking this HLA allele. However, HLA-B27 could be shaping the RA phenotype towards RF seronegativity and axial involvement.
Many of the immune and metabolic changes occurring during normal pregnancy also describe metabolic syndrome. Gut microbiota can cause symptoms of metabolic syndrome in non-pregnant hosts: To explore their role in pregnancy, here we characterized fecal bacteria of 91 pregnant women of varying pre-pregnancy BMIs and gestational diabetes status, and their infants. Similarities between infant-mother microbiotas increased with children’s age, and the infant microbiota was unaffected by mother health status. Gut microbiota changed dramatically from first (T1) to third (T3) trimesters, with vast expansion of diversity between mothers, an overall increase in Proteobacteria and Actinobacteria, and reduced richness. T3 stool showed strongest signs of inflammation and energy loss, however microbiome gene repertoires were constant between trimesters. When transferred to germ-free mice, T3 microbiota induced greater adiposity and insulin insensitivity compared to T1. Our findings indicate that host-microbial interactions impacting host metabolism can occur, and may be beneficial, in pregnancy.
The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit.
Epigenetic modifications are heritable changes in gene expression without changes in DNA sequence. DNA methylation has been implicated in the control of several cellular processes including differentiation, gene regulation, development, genomic imprinting and X-chromosome inactivation. Methylated cytosine residues at CpG dinucleotides are commonly associated with gene repression; conversely, strategic loss of methylation during development could lead to activation of lineage-specific genes. Evidence is emerging that bone development and growth are programmed; although, interestingly, bone is constantly remodelled throughout life. Using human embryonic stem cells, human fetal bone cells (HFBCs), adult chondrocytes and STRO-1+ marrow stromal cells from human bone marrow, we have examined a spectrum of developmental stages of femur development and the role of DNA methylation therein. Using pyrosequencing methodology we analysed the status of methylation of genes implicated in bone biology; furthermore, we correlated these methylation levels with gene expression levels using qRT-PCR and protein distribution during fetal development evaluated using immunohistochemistry. We found that during fetal femur development DNA methylation inversely correlates with expression of genes including iNOS (NOS2) and COL9A1, but not catabolic genes including MMP13 and IL1B. Furthermore, significant demethylation was evident in the osteocalcin promoter between the fetal and adult developmental stages. Increased TET1 expression and decreased expression of DNA (cytosine-5-)-methyltransferase 1 (DNMT1) in adult chondrocytes compared to HFBCs could contribute to the loss of methylation observed during fetal development. HFBC multipotency confirms these cells to be an ideal developmental system for investigation of DNA methylation regulation. In conclusion, these findings demonstrate the role of epigenetic regulation, specifically DNA methylation, in bone development, informing and opening new possibilities in development of strategies for bone repair/tissue engineering.
Infants in Neonatal Intensive Care Units (NICUs) are particularly susceptible to opportunistic infection. Infected infants have high mortality rates, and survivors often suffer life-long neurological disorders. The causes of many NICU infections go undiagnosed, and there is debate as to the importance of inanimate hospital environments (IHEs) in the spread of infections. We used culture-independent next-generation sequencing to survey bacterial diversity in two San Diego NICUs and to track the sources of microbes in these environments. Thirty IHE samples were collected from two Level-Three NICU facilities. We extracted DNA from these samples and amplified the bacterial small subunit (16S) ribosomal RNA gene sequence using ‘universal’ barcoded primers. The purified PCR products were pooled into a single reaction for pyrosequencing, and the data were analyzed using QIIME. On average, we detected 93+/−39 (mean +/− standard deviation) bacterial genera per sample in NICU IHEs. Many of the bacterial genera included known opportunistic pathogens, and many were skin-associated (e.g., Propionibacterium). In one NICU, we also detected fecal coliform bacteria (Enterobacteriales) in a high proportion of the surface samples. Comparison of these NICU-derived sequences to previously published high-throughput 16S rRNA amplicon studies of other indoor environments (offices, restrooms and healthcare facilities), as well as human- and soil-associated environments, found the majority of the NICU samples to be similar to typical building surface and air samples, with the notable exception of the IHEs which were dominated by Enterobacteriaceae. Our findings provide evidence that NICU IHEs harbor a high diversity of human-associated bacteria and demonstrate the potential utility of molecular methods for identifying and tracking bacterial diversity in NICUs.
Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.
Recent work has suggested that individuals can be classified into ‘enterotypes’ based on the abundance of key bacterial taxa in gut microbial communities. However, the generality of enterotypes across populations, and the existence of similar cluster types for other body sites, remains to be evaluated. We combined the Human Microbiome Project 16S rRNA gene sequence data and metagenomes with similar published data to assess the existence of enterotypes across body sites. We found that rather than forming enterotypes (note we use this term for clusters in all body sites), most samples fell into gradients based on taxonomic abundances of bacteria such as Bacteroides, although in some body sites there is a bi/multi modal distribution of samples across gradients. Furthermore, many of the methods used in the analysis (e.g., distance metrics and clustering approaches) affected the likelihood of identifying enterotypes in particular body habitats. We recommend that multiple approaches be used and compared when testing for enterotypes.
A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.
Until recently, the study of microbial diversity has mainly been limited to descriptive approaches, rather than predictive model-based analyses. The development of advanced analytical tools and decreasing cost of high-throughput multi-omics technologies has made the later approach more feasible. However, consensus is lacking as to which spatial and temporal scales best facilitate understanding of the role of microbial diversity in determining both public and environmental health. Here, we review the potential for combining these new technologies with both traditional and nascent spatio-temporal analysis methods. The fusion of proper spatio-temporal sampling, combined with modern multi-omics and computational tools, will provide insight into the tracking, development and manipulation of microbial communities.