The Oral Bacterial Communities of Children with Well-Controlled HIV Infection and without HIV Infection.

The oral microbial community (microbiota) plays a critical role in human health and disease. Alterations in the oral microbiota may be associated with disorders such as gingivitis, periodontitis, childhood caries, alveolar osteitis, oral candidiasis and endodontic infections. In the immunosuppressed population, the spectrum of potential oral disease is even broader, encompassing candidiasis, necrotizing gingivitis, parotid gland enlargement, Kaposi\u27s sarcoma, oral warts and other diseases. Here, we used 454 pyrosequencing of bacterial 16S rRNA genes to examine the oral microbiome of saliva, mucosal and tooth samples from HIV-positive and negative children. Patient demographics and clinical characteristics were collected from a cross-section of patients undergoing routine dental care. Multiple specimens from different sampling sites in the mouth were collected for each patient. The goal of the study was to observe the potential diversity of the oral microbiota among individual patients, sample locations, HIV status and various dental characteristics. We found that there were significant differences in the microbiome among the enrolled patients, and between sampling locations. The analysis was complicated by uneven enrollment in the patient cohorts, with only five HIV-negative patients enrolled in the study and by the rapid improvement in the health of HIV-infected children between the time the study was conceived and completed. The generally good oral health of the HIV-negative patients limited the number of dental plaque samples that could be collected. We did not identify significant differences between well-controlled HIV-positive patients and HIV-negative controls, suggesting that well-controlled HIV-positive patients essentially harbor similar oral flora compared to patients without HIV. Nor were significant differences in the oral microbiota identified between different teeth or with different dental characteristics. Additional studies are needed to better characterize the oral microbiome in children and those with poorly-controlled HIV infections

Muscle Fiber Type-Dependent Differences in the Regulation of Protein Synthesis

This study examined fiber type-dependent differences in the regulation of protein synthesis in individual muscle fibers found within the same whole muscle. Specifically, the in vivo SUrface SEnsing of Translation (SUnSET) methodology was used to measure protein synthesis in type 1, 2A, 2X and 2B fibers of the mouse plantaris muscle, in response to food deprivation (FD), and mechanical overload induced by synergist ablation (SA). The results show that 48 h of FD induced a greater decrease in protein synthesis in type 2X and 2B fibers compared to type 1 and 2A fibers. Type 2X and 2B fibers also had the largest FD-induced decrease in total S6 protein and Ser240/244 S6 phosphorylation, respectively. Moreover, only type 2X and 2B fibers displayed a FD-induced decrease in cross-sectional area (CSA). Ten days of SA also induced fiber type-dependent responses, with type 2B fibers having the smallest SA-induced increases in protein synthesis, CSA and Ser240/244 S6 phosphorylation, but the largest increase in total S6 protein. Embryonic myosin heavy chain (MHCEmb) positive fibers were also found in SA muscles and the protein synthesis rates, levels of S6 Ser240/244 phosphorylation, and total S6 protein content, were 3.6-, 6.1- and 2.9-fold greater than that found in fibers from control muscles, respectively. Overall, these results reveal differential responses in the regulation of protein synthesis and fiber size between fiber types found within the same whole muscle. Moreover, these findings demonstrate that changes found at the whole muscle level do not necessarily reflect changes in individual fiber types

NMDS Coordinate Plots.

<p>NMDS plots were generated using OTU abundance data using the Vegan package in R. Panel A and B contain coordinates labeled by HIV status and sample location respectively, while Panel C and D contain coordinates labeled by dentition status and patient ID.</p

Principle Coordinate Plots.

<p>Principle coordinate plots were generated from OTU abundance data using the Vegan package in R. Panel A demonstrates coordiates from samples labeled by the HIV status. Panel B contains coordinates from samples labeled by the sample location. Panels C and D depict coordinates from samples labeled by dentition status and patient ID respectively.</p

Diversity and Richness Estimators^a.

<p>^a Mean values of Chao1, Shannon and Simpson diversity indexes are shown for all samples with at least one OTU in greater than 25% of all samples and with an n of greater than 4. Standard deviation is shown in parentheses. Total number of samples and OTUs for each site is displayed.</p><p>Diversity and Richness Estimators<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131615#t004fn001" target="_blank">^a</a>.</p

Shannon Diversity Scores.

<p>Boxplots were generated from Shannon diversity scores with quartiles represented in whisker plots and the mean identified by the central point. The Shannon score, which is a measure of species diversity within a sample, is compared between sample locations in Panel A. All Shannon scores for each location, patient or HIV-status are contained in the individual boxplots. Panel B demonstrates that minimal differences were observed between the HIV positive and HIV negative patients. The range of Shannon scores between patients is shown in Panel C. Most patients had scores greater than 5.6. The sole exception was patient 12, who was the only patient under age five. All tooth samples from patient 12 were from primary teeth, and her average shannon score was decreased relative to the other patients with a value of 5.0. All tooth samples are supragingival.</p

Most abundant OTUs.

<p>Relative OTU abundance was summed for primary versus permanent teeth and plaque score. Multiple OTUs may be generated for the same genera, due to the clustering method employed by QIIME. Unique genera consist of OTUs with identical genera assignments, which were summed for each patient group to provide a total abundance for each unique genus. The abundance data was normalized and ranked to identify the ten most abundant genera for each group. Genera identified in only primary or permanent teeth were present in both patient groups, but at lower relative abundance.</p

Patient Demographics^a.

<p>^a<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131615#pone.0131615.t001" target="_blank">Table 1</a> contains demographic information for all patients with sequenced samples. No additional treatment information was available for patient 8. Ages, viral loads and CD4 values were rounded to two significant digits to maintain patient privacy.</p><p>3TC–lamivudine, ABC–abacavir, AZT—d4T = stavudine, ddI–didanosine, EFV–efavirenz, FTC–emtricitabine, LPV/r–lopinavir/ritonavir, NFV–nelfinavir, NVP–nevirapine, TDF–tenofovir, ZDV–zidovudine, SMZ-TMP–sulfamethoxazole and trimethoprim, LGE–linear gingival erythema.</p><p>Patient Demographics<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131615#t001fn001" target="_blank">^a</a>.</p