The Human Sweet Tooth

Humans love the taste of sugar and the word "sweet" is used to describe not only this basic taste quality but also something that is desirable or pleasurable, e.g., la dolce vita. Although sugar or sweetened foods are generally among the most preferred choices, not everyone likes sugar, especially at high concentrations. The focus of my group's research is to understand why some people have a sweet tooth and others do not. We have used genetic and molecular techniques in humans, rats, mice, cats and primates to understand the origins of sweet taste perception. Our studies demonstrate that there are two sweet receptor genes (TAS1R2 and TAS1R3), and alleles of one of the two genes predict the avidity with which some mammals drink sweet solutions. We also find a relationship between sweet and bitter perception. Children who are genetically more sensitive to bitter compounds report that very sweet solutions are more pleasant and they prefer sweet carbonated beverages more than milk, relative to less bitter-sensitive peers. Overall, people differ in their ability to perceive the basic tastes, and particular constellations of genes and experience may drive some people, but not others, toward a caries-inducing sweet diet. Future studies will be designed to understand how a genetic preference for sweet food and drink might contribute to the development of dental caries

Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage

Potential carbon mineralization (Cmin) is a commonly used indicator of soil health, with greater Cmin values interpreted as healthier soil. While Cmin values are typically greater in agricultural soils managed with minimal physical disturbance, the mechanisms driving the increases remain poorly understood. This study assessed bacterial and archaeal community structure and potential microbial drivers of Cmin in soils maintained under various degrees of physical disturbance. Potential carbon mineralization, 16S rRNA sequences, and soil characterization data were collected as part of the North American Project to Evaluate Soil Health Measurements (NAPESHM). Results showed that type of cropping system, intensity of physical disturbance, and soil pH influenced microbial sensitivity to physical disturbance. Furthermore, 28% of amplicon sequence variants (ASVs), which were important in modeling Cmin, were enriched under soils managed with minimal physical disturbance. Sequences identified as enriched under minimal disturbance and important for modeling Cmin, were linked to organisms which could produce extracellular polymeric substances and contained metabolic strategies suited for tolerating environmental stressors. Understanding how physical disturbance shapes microbial communities across climates and inherent soil properties and drives changes in Cmin provides the context necessary to evaluate management impacts on standardized measures of soil microbial activity

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

QTL Analysis of Dietary Obesity in C57BL/6byj X 129P3/J F₂ Mice: Diet- and Sex-Dependent Effects

<div><p>Obesity is a heritable trait caused by complex interactions between genes and environment, including diet. Gene-by-diet interactions are difficult to study in humans because the human diet is hard to control. Here, we used mice to study dietary obesity genes, by four methods. First, we bred 213 F₂ mice from strains that are susceptible [C57BL/6ByJ (B6)] or resistant [129P3/J (129)] to dietary obesity. Percent body fat was assessed after mice ate low-energy diet and again after the same mice ate high-energy diet for 8 weeks. Linkage analyses identified QTLs associated with dietary obesity. Three methods were used to filter candidate genes within the QTL regions: (a) association mapping was conducted using >40 strains; (b) differential gene expression and (c) comparison of genomic DNA sequence, using two strains closely related to the progenitor strains from Experiment 1. The QTL effects depended on whether the mice were male or female or which diet they were recently fed. After feeding a low-energy diet, percent body fat was linked to chr 7 (LOD = 3.42). After feeding a high-energy diet, percent body fat was linked to chr 9 (<i>Obq5</i>; LOD = 3.88), chr 12 (<i>Obq34</i>; LOD = 3.88), and chr 17 (LOD = 4.56). The Chr 7 and 12 QTLs were sex dependent and all QTL were diet-dependent. The combination of filtering methods highlighted seven candidate genes within the QTL locus boundaries: <i>Crx</i>, <i>Dmpk</i>, <i>Ahr</i>, <i>Mrpl28</i>, <i>Glo1</i>, <i>Tubb5</i>, and <i>Mut</i>. However, these filtering methods have limitations so gene identification will require alternative strategies, such as the construction of congenics with very small donor regions.</p></div

Interval maps of chr 7 (low-energy diet) and chr 9, 12, and 17 (high-energy diet), which each contained a QTL identified in the genome-wide scan linked to percent body fat in C57BL/6ByJ x 129P3/J F₂ mice (Experiment 1).

<p>LOD score peaks are marked with arrows, and the associated locus boundaries are indicated by black bars below the peaks. Genetic locations (cM) are based on the experimental map from this genetic cross. Analysis of one- and two-QTL models indicated there are two peaks on chr 9. Significance thresholds are denoted by horizontal lines (for details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068776#pone-0068776-g002" target="_blank"><b>Figure 2</b></a>).</p

QTLs for percent body fat in C57BL/6ByJ×129P3/J F₂ mice (Experiment 1).

<p>Chr = chromosome. cM = centimorgan based on the experimental map. Marker = nearest LOD score peak. “Plus” refers to the allele that increases the trait value. Sex = sex-dependent by the criterion described in the text. For percent variance, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068776#pone-0068776-t004" target="_blank"><b>Table 4</b></a>. For locus boundaries, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068776#pone-0068776-t005" target="_blank"><b>Table 5</b></a>.</p>a<p>Overdominance means that phenotype of heterozygotes differs from phenotypes of both homozygotes. *p<0.05. **p<0.01.</p

Comparison of QTLs from genome scans of B6 × 129 F₂ mice fed high-energy diets (Experiment 1) with published data.

<p>Chr = chromosome. C57BL/6J x 129S1/SvImJ refers to the results of a similar study that interbred these strains <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068776#pone.0068776-Su1" target="_blank">[36]</a>. C57BL/6ByJ x 129P3/J refers to the results reported here. If the locus boundaries overlap, the QTLs are considered the same. Other crosses that have overlapping obesity QTLs are shown with the strain conferring the allele that increases the trait value first. NA = not applicable.</p