Diversity of Bacterial Biofilm Communities on Sprinklers from Dairy Farm Cooling Systems in Israel

<div><p>On dairy farms in hot climates worldwide, cows suffer from heat stress, which is alleviated by the use of water cooling systems. Sprinklers and showerheads are known to support the development of microbial biofilms, which can be a source of infection by pathogenic microorganisms. The aim of this study was to investigate the presence of microbial biofilms in dairy cooling systems, and to analyze their population compositions using culture-independent technique, 16S rRNA gene sequencing. Biofilm samples were collected on eight dairy farms from 40 sprinklers and the microbial constituents were identified by deep sequencing of the 16S rRNA gene. A total of 9,374 operational taxonomic units (OTUs) was obtained from all samples. The mean richness of the samples was 465 ± 268 OTUs which were classified into 26 different phyla; 76% of the reads belonged to only three phyla: Proteobacteria, Actinobacteria and Firmicutes. Although the most prevalent OTUs (<i>Paracoccus</i>, <i>Methyloversatilis</i>, <i>Brevundimonas</i>, <i>Porphyrobacter</i>, Gp4, <i>Mycobacterium</i>, <i>Hyphomicrobium</i>, <i>Corynebacterium</i> and <i>Clostridium</i>) were shared by all farms, each farm formed a unique microbial pattern. Some known potential human and livestock pathogens were found to be closely related to the OTUs found in this study. This work demonstrates the presence of biofilm in dairy cooling systems which may potentially serve as a live source for microbial pathogens.</p></div

Rarefaction curves of 40 filter samples at a cutoff level of 3%.

<p>The rarefaction curve, plotting the number of observed OTUs (sharing ≥97% identity) as a function of the number of sequences, was computed using the RDP Pyrosequencing Pipeline Rarefaction tool.</p

Biofilm in the water sprinklers of dairy farm cooling systems.

<p>(a) Typical cooling system in the holding area of a milking parlor. Water sprinklers above the cows are used to soak/wet the cows. (b) Dismantled sprinkler and its filter. (c) Biofilm sampled from sprinkler filter stained with Acridine orange and visualized by epifluorescence microscopy or (d) cultured on blood agar. Scale bar for panel c is 20 μm.</p

Dairy farms and their microbial abundance.

<p>Names and locations (geographic coordinates) of the dairy farms and relative abundance of the most common phyla as revealed by 16S rRNA gene sequencing analysis. The map was constructed using ArcMap 10.0 software (Esri, Redlands, CA).</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

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

Hitting sets (marker proteins) of 17 microorganism groups.

<p>HS, hitting set. Min., minimal. Greedy and random refer to the algorithm type. Phen., phenotypic. Tax., taxonomic. AIEC, adherent-invasive <i>E</i>. <i>coli</i>. EPEC, enteropathogenic <i>E</i>. <i>coli</i>. UPEC, uropathogenic <i>E</i>. <i>coli</i>. STEC, Shiga toxin-producing <i>E</i>. <i>coli</i>. NMEC, neonatal meningitis-associated <i>E</i>. <i>coli</i>. ExPEC, extra-intestinal pathogenic <i>E</i>. <i>coli</i>. ETEC, enterotoxigenic <i>E</i>. <i>coli</i>. EIEC, enteroinvasive <i>E</i>. <i>coli</i>. EHEC, enterohemorrhagic <i>E</i>. <i>coli</i>. EAEC, enteroaggregative <i>E</i>. <i>coli</i>. APEC, avian pathogenic <i>E</i>. <i>coli</i>. EAHEC, enteroaggregative hemorrhagic <i>E</i>. <i>coli</i>.</p

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

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