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Rheumatoid arthritis (RA) is an autoimmune disease characterized by joint inflammation. Individuals with RA have a higher risk of periodontitis and periodontitis has been linked to RA through the production of enzymes by periodontal pathogens that citrullinate proteins. This linkage is supported by findings that periodontitis is associated with increased RA severity and treatment of periodontitis can improve the symptoms of RA. The possible mechanism for this association is through dysbiosis of the oral microbiota triggered by RA-induced systemic inflammation. We examined the RA status of subjects by measuring the number of tender and swollen joints, anti-citrullinated protein antibody and rheumatoid factor. Periodontal disease status and salivary cytokine levels were measured, and dental plaque analyzed by 16S rRNA high throughput sequencing. RA patients had a higher bacterial load, a more diverse microbiota, an increase in bacterial species associated with periodontal disease, more clinical attachment loss, and increased production of inflammatory mediators including IL-17, IL-2, TNF, and IFN-γ. Furthermore, changes in the oral microbiota were linked to worse RA conditions. Our study provides new insights into the bi-directional relationship between periodontitis and RA and suggest that monitoring the periodontal health of RA patients is particularly important.

Background The oral microbiota is acquired very early, but the factors shaping its acquisition are not well understood. Previous studies comparing monozygotic (MZ) and dizygotic (DZ) twins have suggested that host genetics plays a role. However, all twins share an equal portion of their parent’s genome, so this model is not informative for studying parent-to-child transmission. We used a novel study design that allowed us to directly examine the genetics of transmission by comparing the oral microbiota of biological versus adoptive mother-child dyads. Results No difference was observed in how closely oral bacterial community profiles matched for adoptive versus biological mother-child pairs, indicating little if any effect of host genetics on the fidelity of transmission. Both adopted and biologic children more closely resembled their own mother as compared to unrelated women, supporting the role of contact and environment. Mother-child strain similarity increased with the age of the child, ruling out early effects of host genetic influence that are lost over time. No effect on the fidelity of mother-child strain sharing from vaginal birth or breast feeding was seen. Analysis of extended families showed that fathers and mothers were equally similar to their children, and that cohabitating couples showed even greater strain similarity than mother-child pairs. These findings support the role of contact and shared environment, and age, but not genetics, as determinants of microbial transmission, and were consistent at both species and strain level resolutions, and across multiple oral habitats. In addition, analysis of individual species all showed similar results. Conclusions The host is clearly active in shaping the composition of the oral microbiome, since only a few of the many bacterial species in the larger environment are capable of colonizing the human oral cavity. Our findings suggest that these host mechanisms are universally shared among humans, since no effect of genetic relatedness on fidelity of microbial transmission could be detected. Instead our findings point towards contact and shared environment being the driving factors of microbial transmission, with a unique combination of these factors ultimately shaping the highly personalized human oral microbiome. 1Xj9JJxyfatkyACGqE8HPM Video abstract

Background Sequencing of the 16S rRNA gene has been the standard for studying the composition of microbial communities. While it allows identification of bacteria at the level of species, this method does not usually provide sufficient information to resolve communities at the sub-species level. Species-level resolution is not adequate for studies of transmission or stability or for exploring subspecies variation in disease association. Strain level analysis using whole metagenome shotgun sequencing has significant limitations that can make it unsuitable for large-scale studies. Achieving sufficient depth of sequencing can be cost-prohibitive, and even with adequate coverage, deconvoluting complex communities such as the oral microbiota is computationally very challenging. Thus, there is a need for high-resolution, yet cost-effective, high-throughput methods for characterizing microbial communities. Results Significant improvement in resolution for amplicon-based bacterial community analysis was achieved by combining amplicon sequencing of a high-diversity marker gene, the ribosomal 16-23S intergenic spacer region (ISR), with a probabilistic error modeling based denoising algorithm, DADA2. The resolving power of this new approach was compared to that of both standard and high-resolution 16S-based approaches using a set of longitudinal subgingival plaque samples. The ISR strategy resulted in a 5.2-fold increase in community resolution compared to reference-based 16S rRNA gene analysis and showed 100% accuracy in predicting the correct source of a clinical sample. Individuals’ microbial communities were highly personalized, and although they exhibited some drift in membership and levels over time, that difference was always smaller than the differences between any two subjects, even after 1 year. The construction of an ISR database from publicly available genomic sequences allowed us to explore genomic variation within species, resulting in the identification of multiple variants of the ISR for most species. Conclusions The ISR approach resulted in significantly improved resolution of communities and revealed a highly personalized human oral microbiota that was stable over 1 year. Multiple ISR types were observed for all species examined, demonstrating a high level of subspecies variation in the oral microbiota. The approach is high-throughput, high-resolution yet cost-effective, allowing subspecies-level community fingerprinting at a cost comparable to that of 16S rRNA gene amplicon sequencing. It will be useful for a range of applications that require high-resolution identification of organisms, including microbial tracking, community fingerprinting, and potentially for identification of virulence-associated strains.

The oral microbiome is likely a key element of homeostasis in the oral cavity. With >600 bacterial species and >160 fungal species comprising the oral microbiome, influences on its composition can have an impact on both local and systemic health. The oral microbiome is considered an important factor in health and disease. We recently reported significant effects of HIV and several other clinical variables on the oral bacterial communities in a large cohort of HIV-positive and -negative individuals. The purpose of the present study was to similarly analyze the oral mycobiome in the same cohort. To identify fungi, the internal transcribed spacer 2 (ITS2) of the fungal rRNA genes was sequenced using oral rinse samples from 149 HIV-positive and 88 HIV-negative subjects that had previously undergone bacterial amplicon sequencing. Quantitative PCR was performed for total fungal content and total bacterial content. Interestingly, samples often showed predominance of a single fungal species with four major clusters predominated by Candida albicans , Candida dubliniensis , Malassezia restricta , or Saccharomyces cerevisiae . Quantitative PCR analysis showed the Candida -dominated sample clusters had significantly higher total fungal abundance than the Malassezia or Saccharomyces species. Of the 25 clinical variables evaluated for potential influences on the oral mycobiome, significant effects were associated with caries status, geographical site of sampling, sex, HIV under highly active antiretroviral therapy (HAART), and missing teeth, in rank order of statistical significance. Investigating specific interactions between fungi and bacteria in the samples often showed Candida species positively correlated with Firmicutes or Actinobacteria and negatively correlated with Fusobacteria , Proteobacteria , and Bacteroidetes . Our data suggest that the oral mycobiome, while diverse, is often dominated by a limited number of species per individual; is affected by several clinical variables, including HIV positivity and HAART; and shows genera-specific associations with bacterial groups. IMPORTANCE The oral microbiome is likely a key element of homeostasis in the oral cavity. With >600 bacterial species and >160 fungal species comprising the oral microbiome, influences on its composition can have an impact on both local and systemic health. We recently reported significant effects of HIV and several other clinical variables on the oral bacterial community in a large cohort of HIV-positive and -negative subjects. We describe here a comprehensive analysis of the oral mycobiome in the same cohort. Similar to the bacterial community, HIV under highly active antiretroviral therapy (HAART) had a significant impact on the mycobiome composition, but with less impact compared to other clinical variables. Additionally, unlike the oral bacterial microbiome, the oral mycobiome is often dominated by a single species with 4 major clusters of fungal communities. Together, these results suggest the oral mycobiome has distinct properties compared with the oral bacterial community, although both are equally impacted by HIV.

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous “fingerprints” based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

Following publication of the original article, the authors recognized that the left and right panels in Fig. 6b had been inadvertently switched during reformatting.Following publication of the original article, the authors recognized that the left and right panels in Fig. 6b had been inadvertently switched during reformatting.

We discuss a new class of spatially varying, simultaneous autoregressive (SVSAR) models motivated by interests in flexible, non-stationary spatial modelling scalable to higher dimensions. SVSAR models are hierarchical Markov random fields extending traditional SAR models. We develop Bayesian analysis using Markov chain Monte Carlo methods of SVSAR models, with extensions to spatio-temporal contexts to address problems of data assimilation in computer models. A motivating application in atmospheric science concerns global CO emissions where prediction from computer models is assessed and refined based on high-resolution global satellite imagery data. Application to synthetic and real CO data sets demonstrates the potential of SVSAR models in flexibly representing inhomogeneous spatial processes on lattices, and their ability to improve estimation and prediction of spatial fields. The SVSAR approach is computationally attractive in even very large problems; computational efficiencies are enabled by exploiting sparsity of high-dimensional precision matrices.

Gaussian graphical models (GGMs) are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of GGMs extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous subgroups. In this article, we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable GGMs. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo (MCMC) algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the MCMC algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which MCMC algorithms are too slow to be practically useful.

Hairy roots cultures of Daucus carota turned green under continuous illumination, while the content of the major phenolic compound p-hydroxybenzoic acid (p-HBA) was reduced to half as compared to normal hairy roots cultured in darkness. p-Hydroxybenzaldehyde dehydrogenase (HBD) activity was suppressed in the green hairy roots. However, comparative volatile analysis of 14-day-old green hairy roots revealed higher monoterpene and sesquiterpene contents than found in normal hairy roots. Methyl salicylate content was higher in normal hairy roots than in green ones. Application of clomazone, an inhibitor of 1-deoxy-D-xylulose 5-phosphate synthase (DXS), reduced the amount of total monoterpenes and sesquiterpenes in green hairy roots compared to normal hairy roots. However, methyl salicylate content was enhanced in both green and normal hairy roots treated with clomazone as compared to their respective controls. Because methylerythritol 4-phosphate (MEP) and phenylpropanoid pathways, respectively, contribute to the formation of monoterpenes and phenolic acids biosynthesis, the activities of enzymes regulating those pathways were measured in terms of their in vitro activities, in both green and normal hairy root cultures. These key enzymes were 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), an early regulatory enzyme of the MEP pathway, pyruvate kinase (PK), an enzyme of primary metabolism related to the MEP pathway, shikimate dehydrogenase (SKDH) which is involved in biosynthesis of aromatic amino acids, and phenylalanine ammonia-lyase (PAL) that catalyzes the first step of phenylpropanoid biosynthesis. Activities of DXR and PK were higher in green hairy roots as compared to normal ones, whereas the opposite trend was observed for SKDH and PAL activities. Gene expression analysis of DXR and PAL showed trends similar to those for the respective enzyme activities. Based on these observations, we suggest a possible redirection of metabolites from the primary metabolism toward isoprenoid biosynthesis, limiting the phenolic biosynthetic pathway in green hairy roots grown under continuous light.

In the current scenario, the management of N0 neck in early-stage oral cancer is debatable, whether or not they should undergo elective neck dissection. Most of the time these patients are either over-treated or under-treated. Sentinel lymph node (SLN) biopsy is a good option to identify occult LN in this cohort of patients for guiding neck dissection. With a focus on SLN biopsy using methylene blue dye, this study aims to evaluate its feasibility and accuracy in node-negative oral squamous cell carcinoma. A prospective observational study was conducted involving operable squamous cell carcinoma patients with clinically and radiologically node-negative neck. Methylene blue was injected in the peritumoral area and after that SLN was identified and then neck dissection was completed. Of 47 patients, SLN was identified in 82.98%, with 53.85% having more than two SLN. Common locations were in levels IB, IA and IIA. Occult metastasis was observed in 12.82% of cases, predominantly in T2 patients. Sensitivity, specificity and NPV were 50%, 100% and 88.89% respectively. The study affirms the feasibility and accuracy of methylene blue-assisted SLN biopsy in oral cancer. Despite a high detection rate, methylene blue dye alone should not be used for SLN identification in oral cavity cancer. However, it can be used as an adjunct of lymphoscintigraphy to increase the yield of the procedure. Multi-institutional trials with larger cohorts may provide valuable insights and more information for comprehensively addressing the limitations of this technique and its broader applicability in decision-making, particularly in resource-constrained countries like India where lymphoscintigraphy is not readily accessible.