Use of 16S ribosomal RNA gene analyses to characterize the bacterial signature associated with poor oral health in West Virginia.

BACKGROUND: West Virginia has the worst oral health in the United States, but the reasons for this are unclear. This pilot study explored the etiology of this disparity using culture-independent analyses to identify bacterial species associated with oral disease. METHODS: Bacteria in subgingival plaque samples from twelve participants in two independent West Virginia dental-related studies were characterized using 16S rRNA gene sequencing and Human Oral Microbe Identification Microarray (HOMIM) analysis. Unifrac analysis was used to characterize phylogenetic differences between bacterial communities obtained from plaque of participants with low or high oral disease, which was further evaluated using clustering and Principal Coordinate Analysis. RESULTS: Statistically different bacterial signatures (P<0.001) were identified in subgingival plaque of individuals with low or high oral disease in West Virginia based on 16S rRNA gene sequencing. Low disease contained a high frequency of Veillonella and Streptococcus, with a moderate number of Capnocytophaga. High disease exhibited substantially increased bacterial diversity and included a large proportion of Clostridiales cluster bacteria (Selenomonas, Eubacterium, Dialister). Phylogenetic trees constructed using 16S rRNA gene sequencing revealed that Clostridiales were repeated colonizers in plaque associated with high oral disease, providing evidence that the oral environment is somehow influencing the bacterial signature linked to disease. CONCLUSIONS: Culture-independent analyses identified an atypical bacterial signature associated with high oral disease in West Virginians and provided evidence that the oral environment influenced this signature. Both findings provide insight into the etiology of the oral disparity in West Virginia.

Inter-Species Microbial Sharing in Rural Madagascar: A Study of Environmental Influences on the Skin Microbiome

The skin is home to trillions of microbes, many of which are recently implicated in immune system regulation and various health conditions (33). The skin is continuously exposed to the outside environment, inviting microbial transfer between human skin and the people, animals, and surfaces with which an individual comes into contact. Thus, the aim of this study is to assess how different environmental exposures influence skin microbe communities, as this can strengthen our understanding of how microbial variation relates to health outcomes. This study investigated the skin microbial communities of humans and domesticated cattle living in rural Madagascar. The V3 region of the 16S rRNA gene was sequenced from samples of zebu (the domesticated cattle of Madagascar), zebu owners, and non-zebu owners. Overall, human armpits were the least diverse sample site, while ankles were the most diverse. The diversity of zebu samples was significantly different from armpits, irrespective of zebu ownership (one-way ANOVA and Tukey’s HSD, p<0.05). However, zebu owner samples (from the armpit, ankle forearm, and hand) were more similar to other zebu owner samples than they were to zebu, yet no more similar to other zebu owner samples than they were to non-zebu owner samples (unweighted UniFrac distances, p<0.05). These data suggest a lack of a microbial signature shared by zebu owners and zebu, though further taxonomic analysis is required to explain the role of additional environmental variables in dictating the microbial communities of various samples sites. Understanding the magnitude and directionality of microbial sharing has implications for a breadth of microbe-related health outcomes, with the potential to explain mosquito host preference and mitigate the threats of vector-borne diseases.

The cellular lipids of Romboutsia.

We have examined the lipids of three isolates, Romboutsia lituseburensis, Romboutsia ilealis, and Romboutsia sp. strain FRIFI, of the newly described genus Romboutsia by two-dimensional thin-layer chromatography (2D-TLC) and by liquid chromatography/mass spectrometry (LC/MS). We have found three phospholipids, phosphatidylglycerol (PG), cardiolipin and phosphatidic acid in all three species. A fourth phospholipid, lysyl-PG, was found in R. lituseburensis and strain FRIFI. Polyprenyl-phosphates were identified in the lipid extracts of all three species. Three glycolipids, mono-, di- and tri-hexosyldiacylglycerol, were common to all three species. An additional glycolipid, tetrahexosyl-diacylglycerol was identified in strain FRIFI. Acylated trihexosyldiacylglycerol and acyl-tetrahexosydiacylglycerol were also found in R. ilealis and strain FRIFI. Remarkably, no alk-1-enyl ether lipids (plasmalogens) were present in Romboutsia as distinct from bacteria of the related genus Clostridium in which these ether lipids are common. We have compared the lipidome of Romboutsia with that recently described for Clostridium difficile, which has plasmalogens, no lysyl-PG, and no tetrahexosyl-diacylglycerol. According to 16S rRNA gene sequencing, Romboutsia spp. and C. difficile are closely related (>95% sequence identity).

Polymorphisms at the microRNA binding-site of the stem cell marker gene CD133 modify susceptibility to and survival of gastric cancer.

CD133 is one of the most common stem cell markers, and functional single nucleotide polymorphisms (SNPs) of CD133 may modulate its gene functions and thus cancer risk and patient survival. We hypothesized that potentially functional CD133 SNPs are associated with gastric cancer (GC) risk and survival. To test this hypothesis, we conducted a case-control study of 371 GC patients and 313 cancer-free controls frequency-matched by age, sex, and ethnicity. We genotyped four selected, potentially functional CD133 SNPs (rs2240688A>C, rs7686732C>G, rs10022537T>A, and rs3130C>T) and used logistic regression analysis for associations of these SNPs with GC risk and Cox hazards regression analysis for survival. We found that compared with the miRNA binding site rs2240688 AA genotype, AC + CC genotypes were associated with significantly increased GC risk (adjusted OR = 1.52, 95% CI = 1.09-2.13); for another miRNA binding site rs3130C>T SNP, the TT genotype was associated with significantly reduced GC risk (adjusted OR = 0.68, 95% CI = 0.48-0.97), compared with CC + CT genotypes. In all patients, the risk rs3130 TT variant genotype was significantly associated with overall survival (OS) (adjusted P(trend) = 0.016 and 0.007 under additive and recessive models, respectively). These findings suggest that these two CD133 miRNA binding site variants, rs2240688 and rs3130, may be potential biomarkers for genetic susceptibility to GC and possible predictors for survival in GC patients but require further validation by larger studies.

Evaluating marker-guided treatment selection strategies

© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.