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IDTAXA, for Taxonomic Improving the accuracy of taxonomic classification for identifying taxa in microbiome samples Adithya Murali1, Aniruddha Bhargava2, & Erik S. Wright3 1Department of Computer Sciences, University of Wisconsin–Madison 2Robotics, Amazon, Inc. 3Department of Biomedical Informatics, University of Pittsburgh Contact info: [email protected] WrightLabScience.com 1. Classifying marker gene (e.g., 16S) sequences into a taxonomy Introduction a. Given a reference taxonomy b. The goal is to predict the taxon It has become increasingly clear with sequence representatives of a microbiome sequence that the microbiome is an essential Root new marker gene sequence (e.g., 16S rRNA): component of human and Archaea Bacteria ecosystem health. Microbiome ... AGCGGCAGCACAGAGGAACTTGTTCCTTGG... ... studies frequently involve Phylum: Actinobacteria Bacteroidetes Proteobacteria assign to a taxon sequencing a taxonomic marker, Class: ⍺ β ! " ε ... such as the 16S rRNA or ITS, to ... Order: Aeromonadales Enterobacteriales Legionellales confidences at identify the microorganisms that Root (97.8%); each rank level are present in a sample of interest. Family: Enterobacteriaceae Bacteria (97.8%); ... Proteobacteria (97.8%); We have developed a new method, ... Genus: Citrobacter Erwinia Escherichia Salmonella Gammaproteobacteria (97.8%); named IDTAXA, for taxonomic ... Taxonomic Enterobacteriales (97.8%); classification of marker gene Species: assignment: Enterobacteriaceae (97.8%); Escherichia (95%); sequences that exhibits a E. coli E. coli (82%) substantially lower error rate than E. albertii E. vulneris E. fergusoniiE. marmotae previous approaches. Results 2. How well does the IDTAXA algorithm work for taxonomic assignment? IDTAXA avoids misclassifying a. Comparing performance with b. IDTAXA's confidence is more sequences belonging to novel taxonomic groups that are not different reference taxonomies correlated with percent identity represented in existing taxonomic OC MC OC MC ● ● 1.0 BLAST 1.0 BLAST BLAST−GLOBAL BLAST−GLOBAL IDTAXA SINTAX databases, which is the RDP RDP 0.4 ● SINTAX SINTAX ● 0.8 0.8 ● ● ● SPINGO SPINGO ● ● ● predominant type of error made by ● IDTAXA IDTAXA ● 0.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.6 0.6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● current classifiers. For example, ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● V4 region of RDP RDP training set ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Error rate Error rate ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● 0.4 (genus-level 16S) 0.4 (genus-level 16S) ● the popular RDP Classifier ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.1 ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●●●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● incorrectly assigns 26.0% of novel ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● 0.2 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● 16S rRNA sequences to an existing ● ● 0.0 ● ● ●● ●● 0.0 0.0 taxonomic group when the 0.0OC MC0.2 0.4 0.6 0.8 1.0 0.0OC MC0.2 0.4 0.6 0.8 1.0 ● ● 1.0 BLAST 1.0 BLAST FractionBLAST of classifiable−GLOBAL sequences classified FractionBLAST of classifiable−GLOBAL sequences classified SPINGO RDP organism actually belongs to a RDP RDP 0.4 SINTAX SINTAX 0.8 0.8 ● ● SPINGO SPINGO ● ● novel taxonomic group. In contrast, ● ● IDTAXA IDTAXA ● ● ● ● ● ● ● ● ● 0.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.6 0.6 ● ● ● ● ● ● ● ● ● ● ● IDTAXA only incorrectly classifies ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Warcup training set Contax training set ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Error rate Error rate ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.4 0.4 ● ● ● ● ● ● ● ● ● ● 13.6% of such sequences, while ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (species-level ITS) (genus-level 16S) Distance to classification ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Distance to nearestsequence inclassified group ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● correspondingly improving on the ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●.
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  • Abstract Betaproteobacteria Alphaproteobacteria
    Abstract N-210 Contact Information The majority of the soil’s biosphere containins biodiveristy that remains yet to be discovered. The occurrence of novel bacterial phyla in soil, as well as the phylogenetic diversity within bacterial phyla with few cultured representatives (e.g. Acidobacteria, Anne Spain Dr. Mostafa S.Elshahed Verrucomicrobia, and Gemmatimonadetes) have been previously well documented. However, few studies have focused on the Composition, Diversity, and Novelty within Soil Proteobacteria Department of Botany and Microbiology Department of Microbiology and Molecular Genetics novel phylogenetic diversity within phyla containing numerous cultured representatives. Here, we present a detailed University of Oklahoma Oklahoma State University phylogenetic analysis of the Proteobacteria-affiliated clones identified in a 13,001 nearly full-length 16S rRNA gene clones 770 Van Vleet Oval 307 LSE derived from Oklahoma tall grass prairie soil. Proteobacteria was the most abundant phylum in the community, and comprised Norman, OK 73019 Stillwater, OK 74078 25% of total clones. The most abundant and diverse class within the Proteobacteria was Alphaproteobacteria, which comprised 405 325 5255 405 744 6790 39% of Proteobacteria clones, followed by the Deltaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, which made Anne M. Spain (1), Lee R. Krumholz (1), Mostafa S. Elshahed (2) up 37, 16, and 8% of Proteobacteria clones, respectively. Members of the Epsilonproteobacteria were not detected in the dataset. [email protected] [email protected] Detailed phylogenetic analysis indicated that 14% of the Proteobacteria clones belonged to 15 novel orders and 50% belonged (1) Dept. of Botany and Microbiology, University of Oklahoma, Norman, OK to orders with no described cultivated representatives or were unclassified.
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    Electronic Supplementary Material (ESI) for Environmental Science: Water Research & Technology. This journal is © The Royal Society of Chemistry 2016 Supplementary information for Microbial Electrochemical Systems outperform fixed-bed biofilters for cleaning-up urban wastewater AUTHORS: Arantxa Aguirre-Sierraa, Tristano Bacchetti De Gregorisb, Antonio Berná, Juan José Salasc, Carlos Aragónc, Abraham Esteve-Núñezab* Fig.1S Total nitrogen (A), ammonia (B) and nitrate (C) influent and effluent average values of the coke and the gravel biofilters. Error bars represent 95% confidence interval. Fig. 2S Influent and effluent COD (A) and BOD5 (B) average values of the hybrid biofilter and the hybrid polarized biofilter. Error bars represent 95% confidence interval. Fig. 3S Redox potential measured in the coke and the gravel biofilters Fig. 4S Rarefaction curves calculated for each sample based on the OTU computations. Fig. 5S Correspondence analysis biplot of classes’ distribution from pyrosequencing analysis. Fig. 6S. Relative abundance of classes of the category ‘other’ at class level. Table 1S Influent pre-treated wastewater and effluents characteristics. Averages ± SD HRT (d) 4.0 3.4 1.7 0.8 0.5 Influent COD (mg L-1) 246 ± 114 330 ± 107 457 ± 92 318 ± 143 393 ± 101 -1 BOD5 (mg L ) 136 ± 86 235 ± 36 268 ± 81 176 ± 127 213 ± 112 TN (mg L-1) 45.0 ± 17.4 60.6 ± 7.5 57.7 ± 3.9 43.7 ± 16.5 54.8 ± 10.1 -1 NH4-N (mg L ) 32.7 ± 18.7 51.6 ± 6.5 49.0 ± 2.3 36.6 ± 15.9 47.0 ± 8.8 -1 NO3-N (mg L ) 2.3 ± 3.6 1.0 ± 1.6 0.8 ± 0.6 1.5 ± 2.0 0.9 ± 0.6 TP (mg
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  • Table S4. Phylogenetic Distribution of Bacterial and Archaea Genomes in Groups A, B, C, D, and X
    Table S4. Phylogenetic distribution of bacterial and archaea genomes in groups A, B, C, D, and X. Group A a: Total number of genomes in the taxon b: Number of group A genomes in the taxon c: Percentage of group A genomes in the taxon a b c cellular organisms 5007 2974 59.4 |__ Bacteria 4769 2935 61.5 | |__ Proteobacteria 1854 1570 84.7 | | |__ Gammaproteobacteria 711 631 88.7 | | | |__ Enterobacterales 112 97 86.6 | | | | |__ Enterobacteriaceae 41 32 78.0 | | | | | |__ unclassified Enterobacteriaceae 13 7 53.8 | | | | |__ Erwiniaceae 30 28 93.3 | | | | | |__ Erwinia 10 10 100.0 | | | | | |__ Buchnera 8 8 100.0 | | | | | | |__ Buchnera aphidicola 8 8 100.0 | | | | | |__ Pantoea 8 8 100.0 | | | | |__ Yersiniaceae 14 14 100.0 | | | | | |__ Serratia 8 8 100.0 | | | | |__ Morganellaceae 13 10 76.9 | | | | |__ Pectobacteriaceae 8 8 100.0 | | | |__ Alteromonadales 94 94 100.0 | | | | |__ Alteromonadaceae 34 34 100.0 | | | | | |__ Marinobacter 12 12 100.0 | | | | |__ Shewanellaceae 17 17 100.0 | | | | | |__ Shewanella 17 17 100.0 | | | | |__ Pseudoalteromonadaceae 16 16 100.0 | | | | | |__ Pseudoalteromonas 15 15 100.0 | | | | |__ Idiomarinaceae 9 9 100.0 | | | | | |__ Idiomarina 9 9 100.0 | | | | |__ Colwelliaceae 6 6 100.0 | | | |__ Pseudomonadales 81 81 100.0 | | | | |__ Moraxellaceae 41 41 100.0 | | | | | |__ Acinetobacter 25 25 100.0 | | | | | |__ Psychrobacter 8 8 100.0 | | | | | |__ Moraxella 6 6 100.0 | | | | |__ Pseudomonadaceae 40 40 100.0 | | | | | |__ Pseudomonas 38 38 100.0 | | | |__ Oceanospirillales 73 72 98.6 | | | | |__ Oceanospirillaceae
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  • Which Organisms Are Used for Anti-Biofouling Studies
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    ParST is a widespread toxin–antitoxin module that targets nucleotide metabolism Frank J. Piscottaa, Philip D. Jeffreyb, and A. James Linka,b,c,1 aDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544; bDepartment of Molecular Biology, Princeton University, Princeton, NJ 08544; and cDepartment of Chemistry, Princeton University, Princeton, NJ 08544 Edited by Marlene Belfort, University at Albany, Albany, NY, and approved December 4, 2018 (received for review August 27, 2018) Toxin–antitoxin (TA) systems interfere with essential cellular pro- I toxins are typified by an RNA-level TA interaction, where an cesses and are implicated in bacterial lifestyle adaptations such as antisense RNA binds toxin mRNA to inhibit its translation. Type persistence and the biofilm formation. Here, we present structural, II TA systems function via protein–protein interactions, where biochemical, and functional data on an uncharacterized TA system, binding of the antitoxin to the toxin inhibits its activity (10). Of the COG5654–COG5642 pair. Bioinformatic analysis showed that the two earliest known TA systems mentioned above, hok/sok is this TA pair is found in 2,942 of the 16,286 distinct bacterial species an example of a type I system, while ccdAB is type II (11, 12). in the RefSeq database. We solved a structure of the toxin bound Upon translation, type I toxins are small, hydrophobic peptides to a fragment of the antitoxin to 1.50 Å. This structure suggested that lead to cell lysis through disruption of the plasma mem- that the toxin is a mono-ADP-ribosyltransferase (mART). The toxin brane. Type II toxins, however, function through a variety of specifically modifies phosphoribosyl pyrophosphate synthetase mechanisms.
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  • Host-Adaptation in Legionellales Is 2.4 Ga, Coincident with Eukaryogenesis
    bioRxiv preprint doi: https://doi.org/10.1101/852004; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 Host-adaptation in Legionellales is 2.4 Ga, 2 coincident with eukaryogenesis 3 4 5 Eric Hugoson1,2, Tea Ammunét1 †, and Lionel Guy1* 6 7 1 Department of Medical Biochemistry and Microbiology, Science for Life Laboratories, 8 Uppsala University, Box 582, 75123 Uppsala, Sweden 9 2 Department of Microbial Population Biology, Max Planck Institute for Evolutionary 10 Biology, D-24306 Plön, Germany 11 † current address: Medical Bioinformatics Centre, Turku Bioscience, University of Turku, 12 Tykistökatu 6A, 20520 Turku, Finland 13 * corresponding author 14 1 bioRxiv preprint doi: https://doi.org/10.1101/852004; this version posted February 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 15 Abstract 16 Bacteria adapting to living in a host cell caused the most salient events in the evolution of 17 eukaryotes, namely the seminal fusion with an archaeon 1, and the emergence of both the 18 mitochondrion and the chloroplast 2. A bacterial clade that may hold the key to understanding 19 these events is the deep-branching gammaproteobacterial order Legionellales – containing 20 among others Coxiella and Legionella – of which all known members grow inside eukaryotic 21 cells 3.
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  • Wedding Higher Taxonomic Ranks with Metabolic Signatures Coded in Prokaryotic Genomes
    Wedding higher taxonomic ranks with metabolic signatures coded in prokaryotic genomes Gregorio Iraola*, Hugo Naya* Corresponding authors: E-mail: [email protected], [email protected] This PDF file includes: Supplementary Table 1 Supplementary Figures 1 to 4 Supplementary Methods SUPPLEMENTARY TABLES Supplementary Tab. 1 Supplementary Tab. 1. Full prediction for the set of 108 external genomes used as test. genome domain phylum class order family genus prediction alphaproteobacterium_LFTY0 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Unknown candidatus_nasuia_deltocephalinicola_PUNC_CP013211 Bacteria Proteobacteria Gammaproteobacteria Unknown Unknown Unknown candidatus_sulcia_muelleri_PUNC_CP013212 Bacteria Bacteroidetes Flavobacteriia Flavobacteriales NA Candidatus Sulcia deinococcus_grandis_ATCC43672_BCMS0 Bacteria Deinococcus-Thermus Deinococci Deinococcales Deinococcaceae Deinococcus devosia_sp_H5989_CP011300 Bacteria Proteobacteria Unknown Unknown Unknown Unknown micromonospora_RV43_LEKG0 Bacteria Actinobacteria Actinobacteria Micromonosporales Micromonosporaceae Micromonospora nitrosomonas_communis_Nm2_CP011451 Bacteria Proteobacteria Betaproteobacteria Nitrosomonadales Nitrosomonadaceae Unknown nocardia_seriolae_U1_BBYQ0 Bacteria Actinobacteria Actinobacteria Corynebacteriales Nocardiaceae Nocardia nocardiopsis_RV163_LEKI01 Bacteria Actinobacteria Actinobacteria Streptosporangiales Nocardiopsaceae Nocardiopsis oscillatoriales_cyanobacterium_MTP1_LNAA0 Bacteria Cyanobacteria NA Oscillatoriales
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  • Monitoring Distribution Systems for Legionella Pneumophila Using Legiolert
    Received: 21 September 2018 Revised: 26 November 2018 Accepted: 17 December 2018 DOI: 10.1002/aws2.1122 ORIGINAL RESEARCH Monitoring distribution systems for Legionella pneumophila using Legiolert Mark W. LeChevallier Dr. Water Consulting, Morrison, Colorado This study implemented the Legiolert test (a culture-based assay for L. pneumo- Correspondence Mark W. LeChevallier, Dr. Water Consulting, phila based on the most probable number [MPN]) at 12 utilities to assess their Morrison, Colorado. experiences and to develop a baseline of Legionella pneumophila occurrence in Email: [email protected] drinking water distribution systems. A total of 679 samples were analyzed during Funding information the study: 53 source water, 50 from the plant effluent, and 576 from the distribution IDEXX Laboratories, Westbrook, ME system. L. pneumophila was detected in three of five source water samples at one utility but was not detected in any of the treated plant effluent samples. L. pneumophila was detected in only one distribution sample (0.17%) at a concen- tration of 1 MPN/100 mL in a sample that contained 0.72 mg/L free chlorine and was serotyped as belonging to the 2–14 serogroup. Four (0.7%) distribution sam- ples could not be confirmed by serotyping. Overall, the utilities found the test easy to learn and apply in their systems. This study provides a precedent for future mon- itoring of drinking water systems. KEYWORDS distribution, L. pneumophila, Legiolert, Legionella, microbiology 1 | INTRODUCTION L. pneumophila has become the most commonly identi- fied drinking water pathogen, responsible for about two-thirds Legionellosis is a respiratory infection caused by bacteria in of all potable water outbreaks and nearly all the fatalities asso- the genus Legionella.
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