Data_Sheet_1_Enhanced Bacterial Fitness Under Residual Fluoroquinolone Concentrations Is Associated With Increased Gene Expression in Wastewater-Derived qnr Plasmid-Harboring Strains.docx

<p>Plasmids harboring qnr genes confer resistance to low fluoroquinolone concentrations. These genes are of significant clinical, evolutionary and environmental importance, since they are widely distributed in a diverse array of natural and clinical environments. We previously extracted and sequenced a large (∼185 Kbp) qnrB-harboring plasmid, and several small (∼8 Kbp) qnrS-harboring plasmids, from Klebsiella pneumoniae isolates from municipal wastewater biosolids, and hypothesized that these plasmids provide host bacteria a selective advantage in wastewater treatment plants (WWTPs) that often contain residual concentrations of fluoroquinolones. The objectives of this study were therefore to determine the effect of residual fluoroquinolone concentrations on the growth kinetics of qnr plasmid-harboring bacteria; and on the copy number of qnr plasmids and expression of qnr genes. Electrotransformants harboring either one of the two types of plasmids could grow at ciprofloxacin concentrations exceeding 0.5 μg ml^-1, but growth was significantly decreased at concentrations higher than 0.1 μg ml^-1. In contrast, plasmid-free strains failed to grow even at 0.05 μg ml^-1. No differences were observed in plasmid copy number under the tested ciprofloxacin concentrations, but qnr expression increased incrementally from 0 to 0.4 μg ml^-1, suggesting that the transcription of this gene is regulated by antibiotic concentration. This study reveals that wastewater-derived qnr plasmids confer a selective advantage in the presence of residual fluoroquinolone concentrations and provides a mechanistic explanation for this phenomenon.</p

A New Comparative-Genomics Approach for Defining Phenotype-Specific Indicators Reveals Specific Genetic Markers in Predatory Bacteria

<div><p>Predatory bacteria seek and consume other live bacteria. Although belonging to taxonomically diverse groups, relatively few bacterial predator species are known. Consequently, it is difficult to assess the impact of predation within the bacterial realm. As no genetic signatures distinguishing them from non-predatory bacteria are known, genomic resources cannot be exploited to uncover novel predators. In order to identify genes specific to predatory bacteria, we developed a bioinformatic tool called DiffGene. This tool automatically identifies marker genes that are specific to phenotypic or taxonomic groups, by mapping the complete gene content of all available fully-sequenced genomes for the presence/absence of each gene in each genome. A putative ‘predator region’ of ~60 amino acids in the tryptophan 2,3-dioxygenase (TDO) protein was found to probably be a predator-specific marker. This region is found in all known obligate predator and a few facultative predator genomes, and is absent from most facultative predators and all non-predatory bacteria. We designed PCR primers that uniquely amplify a ~180bp-long sequence within the predators’ TDO gene, and validated them in monocultures as well as in metagenetic analysis of environmental wastewater samples. This marker, in addition to its usage in predator identification and phylogenetics, may finally permit reliable enumeration and cataloguing of predatory bacteria from environmental samples, as well as uncovering novel predators.</p></div

A Global Transcriptional Switch between the Attack and Growth Forms of <i>Bdellovibrio bacteriovorus</i>

<div><p><i>Bdellovibrio bacteriovorus</i> is an obligate predator of bacteria ubiquitously found in the environment. Its life cycle is composed of two essential phases: a free-living, non-replicative, fast swimming attack phase (AP) wherein the predator searches for prey; and a non-motile, actively dividing growth phase (GP) in which it consumes the prey. The molecular regulatory mechanisms governing the switch between AP and GP are largely unknown. We used RNA-seq to generate a single-base-resolution map of the <i>Bdellovibrio</i> transcriptome in AP and GP, revealing a specific "AP" transcriptional program, which is largely mutually exclusive of the GP program. Based on the expression map, most genes in the <i>Bdellovibrio</i> genome are classified as "AP only" or "GP only". We experimentally generated a genome-wide map of 140 AP promoters, controlling the majority of AP-specific genes. This revealed a common sigma-like DNA binding site highly similar to the <i>E. coli</i> flagellar genes regulator sigma28 (FliA). Further analyses suggest that FliA has evolved to become a global AP regulator in <i>Bdellovibrio</i>. Our results also reveal a non-coding RNA that is massively expressed in AP. This ncRNA contains a c-di-GMP riboswitch. We suggest it functions as an intracellular reservoir for c-di-GMP, playing a role in the rapid switch from AP to GP.</p></div

Automatic identification of optimal marker genes for phenotypic and taxonomic groups of microorganisms - Fig 1

<p>Graphical representation of the proteins (denoted P₁, P₂, P₃, P₄, P₅) which can serve as markers for the bacterial (denoted B₁, B₂, B₃, B₄) group of interest consisting of B₁ and B₂: (A) shows that P₁, P₂ can serve as a minimal set of markers for the group of interest; (B) P₁ only can serve as a marker for the group of interest; and (C) there are no markers for the group of interest.</p

Automatic identification of optimal marker genes for phenotypic and taxonomic groups of microorganisms

<div><p>Finding optimal markers for microorganisms important in the medical, agricultural, environmental or ecological fields is of great importance. Thousands of complete microbial genomes now available allow us, for the first time, to exhaustively identify marker proteins for groups of microbial organisms. In this work, we model the biological task as the well-known mathematical “hitting set” problem, solving it based on both greedy and randomized approximation algorithms. We identify unique markers for 17 phenotypic and taxonomic microbial groups, including proteins related to the nitrite reductase enzyme as markers for the non-anammox nitrifying bacteria group, and two transcription regulation proteins, <i>nusG</i> and <i>yhiF</i>, as markers for the Archaea and <i>Escherichia/Shigella</i> taxonomic groups, respectively. Additionally, we identify marker proteins for three subtypes of pathogenic <i>E</i>. <i>coli</i>, which previously had no known optimal markers. Practically, depending on the completeness of the database this algorithm can be used for identification of marker genes for any microbial group, these marker genes may be prime candidates for the understanding of the genetic basis of the group's phenotype or to help discover novel functions which are uniquely shared among a group of microbes. We show that our method is both theoretically and practically efficient, while establishing an upper bound on its time complexity and approximation ratio; thus, it promises to remain efficient and permit the identification of marker proteins that are specific to phenotypic or taxonomic groups, even as more and more bacterial genomes are being sequenced.</p></div

RT-PCR verification of mutually exclusive expression.

<p>Total RNA retrieved from AP or GP <i>B. bacteriovorus</i> HD100 during a synchronous predation of <i>E. coli</i> ML35 (0.5, 1 and 3 hrs post inoculation) was subjected to RT-PCR. Sixteen representative genes predicted by RNA-seq analysis to be AP-specific (left) or GP-specific (right) were amplified. Coli, control genomic DNA of <i>E. coli</i> ML35; AP, cDNA from AP cells; GP0.5, cDNA of GP cells 0.5 hr post inoculation; GP1, cDNA of GP cells 1 hr post inoculation; GP3, cDNA of GP cells 3 hrs post inoculation. DNA, <i>B. bacteriovorus</i> genomic DNA.</p

Maximum-likelihood phylogenetic tree of the tryptophan 2,3-dioxygenase protein.

<p>The percentage of trees in which the associated taxa clustered together (out of 100 bootstraps) is shown next to the branches; branches with <50% were collapsed. Obligate bacterial predators are marked orange, facultative yellow. Red line indicates genomes with the ~60 amino acid-long insert.</p

Software structure and output.

<p>Software structure and output.</p

Maximum-likelihood phylogenetic tree of the representative sequences of the 100-most abundant OTUs in the metagenetic analysis, including representative TDO sequences from known predatory and non-predatory bacteria.

<p>The bootstrap consensus tree inferred from 100 replicates is taken to represent the evolutionary history of the taxa analyzed. Wastewater OTU names are according to abundance, i.e. OTU1 is the most abundant OTU in the environmental samples, OTU2 is the second-most abundant, and so on. Known sequence names include GI accession, coordinates within the genome, and species name.</p

AP-specific small RNAs.

<p>AP-specific small RNAs.</p