Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies.

BACKGROUND: The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82]. RESULTS: At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%). CONCLUSIONS: Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Optimization of synthesis of carbohydrates and 1-phenyl-3-methyl-5-pyrazolone (PMP) by response surface methodology (RSM) for improved carbohydrate detection.

Reducing sugars can react with 1-phenyl-3-methyl-5-pyrazolone (PMP) to form sugar-PMP derivatives, which can be detected by HPLC-UV or HPLC-DAD due to their high UV absorbance at 248 nm. Six different sugars were synthesized with PMP with aid of response surface methodology (RSM), by which the parameters of the synthesis were designed within temperature ranged between 60 °C and 90 °C, and time from 60 to 180 min, respectively. Consequently, optimal conditions of the glucose (Glu)-, glucosamine (GluN)-, galactose (Gal)-, glucuronic acid (GluA), galacturonic acid (GalA) and glucose-6-phosphate (G6P-PMP) reactions were determined at 71 °C for 129 min, 73 °C for 96 min, 70 °C for 117 min, 75 °C for 151 min, 76 °C for 144 min, and 70 °C for 154 min, respectively. Experiments demonstrated that unique functional groups and delicate differences of carbohydrates' inner pH environment could significantly influence the sugar-PMP reactions. However, sugar stereoisomers did not have remarkable impacts on the reactions.

Spatially Modeling the Effects of Meteorological Drivers of PM2.5 in the Eastern United States via a Local Linear Penalized Quantile Regression Estimator.

Fine particulate matter (PM2.5) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM2.5. It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM2.5 over the Eastern United States (US). This analysis suggests that the primary drivers throughout much of the Eastern US tend to differ based on season and geographic location, with similarities existing between "typical" and "high" PM2.5 levels.