Nasopharyngeal Microbiota Profiles in Rural Venezuelan Children Are Associated With Respiratory and Gastrointestinal Infections

BACKGROUND: Recent research suggests that the microbiota affects susceptibility to both respiratory tract infections (RTIs) and gastrointestinal infections (GIIs). In order to optimize global treatment options, it is important to characterize microbiota profiles across different niches and geographic/socioeconomic areas where RTI and GII prevalences are high. METHODS: We performed 16S sequencing of nasopharyngeal swabs from 209 Venezuelan Amerindian children aged 6 weeks-59 months who were participating in a 13-valent pneumococcal conjugate vaccine (PCV13) study. Using random forest models, differential abundance testing, and regression analysis, we determined whether specific bacteria were associated with RTIs or GIIs and variation in PCV13 response. RESULTS: Microbiota compositions differed between children with or without RTIs (P = .018) or GIIs (P = .001). Several species were associated with the absence of infections. Some of these health-associated bacteria are also observed in developed regions, such as Corynebacterium (log2(fold change [FC]) = 3.30 for RTIs and log2(FC) = 1.71 for GIIs), while others are not commonly observed in developed regions, such as Acinetobacter (log2(FC) = 2.82 and log2(FC) = 5.06, respectively). Klebsiella spp. presence was associated with both RTIs (log2(FC) = 5.48) and GIIs (log2(FC) = 7.20). CONCLUSIONS: The nasopharyngeal microbiota of rural Venezuelan children included several bacteria that thrive in tropical humid climates. Interestingly, nasopharyngeal microbiota composition not only differed in children with an RTI but also in those with a GII, which suggests a reciprocal interplay between the 2 environments. Knowledge of region-specific microbiota patterns enables tailoring of preventive and therapeutic approaches

Predicting olfactory receptor neuron responses from odorant structure

Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusions The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their 'receptive fields'. Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data