Temporal and Spatial Diversity of the Tap Water Microbiota in a Norwegian Hospital

We analyzed the temporal and spatial diversity of the microbiota in a low-usage and a high-usage hospital tap. We identified a tap-specific colonization pattern, with potential human pathogens being overrepresented in the low-usage tap. We propose that founder effects and local adaptation caused the tap-specific colonization patterns. Our conclusion is that tap-specific colonization represents a potential challenge for water safety.Humans are exposed to and consume large amounts of tap water in their everyday life, with the tap water microbiota representing a potent reservoir for pathogens (8). Despite the potential impact, our knowledge about the ecological diversification processes of the tap water microbiota is limited (4, 11).The aim of the present work was to determine the temporal and spatial distribution patterns of the planktonic tap water microbiota. We compared the summer and winter microbiota from two hospital taps supplied from the same water source. We analyzed 16S rRNA gene clone libraries by using a novel alignment-independent approach for operational taxonomic unit (OTU) designation (6), while established OTU diversity and richness estimators were used for the ecological interpretations.Tap water samples (1 liter) from a high-usage kitchen and a low-usage toilet cold-water tap in Akershus University Hospital, Lørenskog, Norway, were collected in January and July 2006. The total DNA was isolated and the 16S rRNA gene PCR amplified and sequenced. Based on the sequences, we estimated the species richness and diversity, we calculated the distances between the communities, and trees were constructed to reflect the relatedness of the microbiota in the samples analyzed. Details about these analytical approaches are given in the materials and methods section in the supplemental material.Our initial analysis of species composition was done using the RDPII hierarchical classifier. We found that the majority of pathogen-related bacteria in our data set belonged to the class Gammaproteobacteria. The genera encompassed Legionella, Pseudomonas, and Vibrio (Table ​(Table1).1). We found a significant overrepresentation of pathogen-related bacteria in the toilet tap (P = 0.04), while there were no significant differences between summer and winter samples. Legionella showed the highest relative abundance for the pathogen-related bacteria. With respect to the total diversity, we found that Proteobacteria dominated the tap water microbiota (representing 86% of the taxa) (see Table S1 in the supplemental material). There was, however, a large portion (56%) of the taxa that could not be assigned to the genus level using this classifier.

TABLE 1.

Cloned sequences related to human pathogens^a

Unsuitability of Quantitative Bacteroidales 16S rRNA Gene Assays for Discerning Fecal Contamination of Drinking Water

Bacteroidales species were detected in (tap) water samples from treatment plants with three different PCR assays. 16S rRNA gene sequence analysis indicated that the sequences had an environmental rather than fecal origin. We conclude that assays for Bacteroidales 16S rRNA genes are not specific enough to discern fecal contamination of drinking water in the Netherlands.Drinking water in many countries is routinely monitored for recent fecal contamination by testing for fecal indicator organisms Escherichia coli, thermotolerant coliforms, and/or intestinal enterococci to demonstrate microbial safety (13, 21, 42). Although these indicator organisms have been used for many decades, they have some limitations: the number of E. coli/coliform/enterococcus bacteria in feces is relatively low (18, 38), and they sometimes might be able to grow in the environment (10, 11, 14, 27). Consequently, scientists have been searching for alternative indicator organisms to determine fecal contamination of water. In 1967, bacteria belonging to the genus Bacteroides were suggested as alternative indicator organisms (26). Bacteroides spp. might have some advantages over the traditional indicator organisms. The numbers of Bacteroides spp. in the intestinal tract of humans and animals are 10 to 100 times higher than the numbers of E. coli or intestinal enterococci (1, 2, 12, 26). However, the use of Bacteroides spp. as indicator organisms was hampered by the complex cultivation conditions required (1, 2). The introduction of molecular methods made it possible to detect bacterial species that belong to the order Bacteroidales, an order that includes the genus Bacteroides, without cultivation. As a result, real-time PCR methods were developed for the quantitative detection of Bacteroidales in surface and recreation water and the potential of Bacteroidales species as an indication of fecal contamination of recreational waters was demonstrated (6, 12, 16, 19, 20, 29). Bacteroidales species might be useful indicator organisms for fecal contamination of drinking water as well. However, methods to detect fecal contamination in drinking water should be more sensitive, because people ingest more drinking water and the quality assessments and standards for fecal contamination are stricter than for bathing water. Studies exploring real-time PCR for the detection of Bacteroidales genes in drinking water have not been published to our knowledge. The objective of our study was, therefore, to determine if assays for the detection of Bacteroidales 16S rRNA genes can be used to detect fecal contamination in drinking water.Unchlorinated tap water samples were obtained in November 2007 and February 2010 from one or more locations in the distribution systems of nine different drinking water treatment plants (plants A to I; Table ​Table1)1) that produced unchlorinated drinking water from confined (plants B, C, E, F, and G) and unconfined (plants A, D, H, and I) groundwater. The treatment plants are located in the central part of the Netherlands within 90 km of each other. In addition, untreated groundwater from extraction wells and/or untreated raw groundwater (mixture of groundwater from different extraction wells) was sampled in March 2008 (Table ​(Table1).1). Water samples (100 ml) were filtered over a 25-mm polycarbonate filter (0.22-μm pore size, type GTTP; Millipore, Netherlands) and a DNA fragment was added as internal control to determine the recovery efficiency of DNA isolation and PCR analysis (2a, 40). DNA was isolated using a FastDNA spin kit for soil (Qbiogene, United States) according to the supplier''s protocol. Primer sets AllBac 296f and AllBac 412r, resulting in a PCR product of 108 bp, were used in combination with TaqMan probe AllBac375Bhqr to quantitatively determine the number of Bacteroidales 16S rRNA gene copies in the water samples using a real-time PCR instrument (20). The PCR cycle after which the fluorescence signal of the amplified DNA was detected (threshold cycle [C_T]) was used to quantify the concentration of 16S rRNA gene copies. Quantification was based on comparison of the sample C_T value with the C_T values of a calibration curve graphed using known copy numbers of the Bacteroidales 16S rRNA gene, as previously described (12, 20). The correlation coefficient of the calibration curve was 0.99, and the efficiency of the PCR 95 to 105%. Finally, the Bacteroidales cell number was calculated by using the recovery rate of the internal standard and assuming five 16S rRNA gene copy numbers per cell (5). The detection limit of this gene assay was 50 Bacteroidales cells 100 ml⁻¹ (corresponding to 10 16S rRNA gene copies per reaction mixture). Furthermore, the 16S rRNA genes that were obtained from several water samples from treatment plant C with the AllBac and TotBac (12) primer sets were sequenced, and the nearest relatives were obtained from the GenBank database using BLAST searches.

TABLE 1.

Numbers of Bacteroidales cells in extraction wells, raw groundwater, and unchlorinated tap water of nine different groundwater plants in the Netherlands^a

Evidence for Interspecies Gene Transfer in the Evolution of 2,4-Dichlorophenoxyacetic Acid Degraders

Small-subunit ribosomal DNA (SSU rDNA) from 20 phenotypically distinct strains of 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacteria was partially sequenced, yielding 18 unique strains belonging to members of the alpha, beta, and gamma subgroups of the class Proteobacteria. To understand the origin of 2,4-D degradation in this diverse collection, the first gene in the 2,4-D pathway, tfdA, was sequenced. The sequences fell into three unique classes found in various members of the beta and gamma subgroups of Proteobacteria. None of the α-Proteobacteria yielded tfdA PCR products. A comparison of the dendrogram of the tfdA genes with that of the SSU rDNA genes demonstrated incongruency in phylogenies, and hence 2,4-D degradation must have originated from gene transfer between species. Only those strains with tfdA sequences highly similar to the tfdA sequence of strain JMP134 (tfdA class I) transferred all the 2,4-D genes and conferred the 2,4-D degradation phenotype to a Burkholderia cepacia recipient.Bacteria capable of mineralizing 2,4-dichlorophenoxyacetic acid (2,4-D), a commonly used herbicide, are found in many different phylogenetic groups (2, 3, 7, 11, 22, 23). Evidence suggests that numerous variants of 2,4-D catabolic genes exist and that catabolic operons consist of a near-random mixing of these variants (7). Interspecies gene transfer is a well-documented phenomenon (13), and horizontal gene transfer of the 2,4-D-degrading plasmid pJP4 has been shown (3, 5). However, not all 2,4-D catabolic operons are found on plasmids (10, 11, 16, 20). The extent to which other 2,4-D genes have been exchanged in nature is unknown. The aim of this research was to assess the role of horizontal gene transfer in the evolution of 2,4-D-degrading strains. This article summarizes the results of two aspects of this work—the study of the transfer of the entire 2,4-D pathway by using standard mating experiments and a phylogenetic study of the tfdA gene. The tfdA gene codes for an α-ketoglutarate-dependent 2,4-D dioxygenase which converts 2,4-D into 2,4-dichlorophenol and glyoxylate (6). This 861-bp gene was first sequenced from Ralstonia eutropha JMP134 (19). Two more tfdA genes were cloned from chromosomal locations in Burkholderia strain RASC and Burkholderia strain TFD6 (16, 20). These proved to be identical to each other and 78.5% similar to the original. An alignment of the two variants allowed conserved areas to be identified and primers to be designed for the amplification of tfdA-like genes from other sources (24). Sequence analysis of putative tfdA fragments and the small-subunit ribosomal DNA (SSU rDNA) of the strains carrying them allowed us to construct phylogenies of the genes and their hosts and to look for congruency between them.

Mating experiments.

A collection of 2,4-D degraders containing 15 unique strains as determined by genomic fingerprinting (7) was used as a source of donors in a series of mating experiments (Table ​(Table1).1). Burkholderia cepacia D5, lacking the ability to grow on 2,4-D and not hybridizing to any tfd genes, was used as a recipient in mating experiments. Strain D5 contains neomycin phosphotransferase genes (nptII) carried on transposon Tn5 and is resistant to 50 μg each of kanamycin, carbenicillin, and bacitracin per ml. All of the 2,4-D strains used were sensitive to these antibiotics. Filter matings were performed with a donor-to-recipient ratio of 1:10. Colonies which grew on selective medium (500 ppm of 2,4-D in mineral salts agar [MMO] [23] including 50 μg of kanamycin, carbenicillin, and bacitracin per ml) were subjected to further tests. Their ability to catabolize 2,4-D was tested in liquid medium (same composition as that described above).

TABLE 1

2,4-D-degrading strains, geographic origins, and GenBank accession numbers

Stress-induced flowering

Many plant species can be induced to flower by responding to stress factors. The short-day plants Pharbitis nil and Perilla frutescens var. crispa flower under long days in response to the stress of poor nutrition or low-intensity light. Grafting experiments using two varieties of P. nil revealed that a transmissible flowering stimulus is involved in stress-induced flowering. The P. nil and P. frutescens plants that were induced to flower by stress reached anthesis, fruited and produced seeds. These seeds germinated, and the progeny of the stressed plants developed normally. Phenylalanine ammonialyase inhibitors inhibited this stress-induced flowering, and the inhibition was overcome by salicylic acid (SA), suggesting that there is an involvement of SA in stress-induced flowering. PnFT2, a P. nil ortholog of the flowering gene FLOWERING LOCUS T (FT) of Arabidopsis thaliana, was expressed when the P. nil plants were induced to flower under poor-nutrition stress conditions, but expression of PnFT1, another ortholog of FT, was not induced, suggesting that PnFT2 is involved in stress-induced flowering.Key words: flowering, stress, phenylalanine ammonia-lyase, salicylic acid, FLOWERING LOCUS T, Pharbitis nil, Perilla frutescensFlowering in many plant species is regulated by environmental factors, such as night-length in photoperiodic flowering and temperature in vernalization. On the other hand, a short-day (SD) plant such as Pharbitis nil (synonym Ipomoea nil) can be induced to flower under long days (LD) when grown under poor-nutrition, low-temperature or high-intensity light conditions.^1–9 The flowering induced by these conditions is accompanied by an increase in phenylalanine ammonia-lyase (PAL) activity.¹⁰ Taken together, these facts suggest that the flowering induced by these conditions might be regulated by a common mechanism. Poor nutrition, low temperature and high-intensity light can be regarded as stress factors, and PAL activity increases under these stress conditions.¹¹ Accordingly, we assumed that such LD flowering in P. nil might be induced by stress. Non-photoperiodic flowering has also been sporadically reported in several plant species other than P. nil, and a review of these studies suggested that most of the factors responsible for flowering could be regarded as stress. Some examples of these factors are summarized in 12^–14

Table 1

Some cases of stress-induced flowering

Vast (but avoidable) underestimation of global biodiversity

The number of species on Earth is highly uncertain. A recent study has suggested that there are less than 2 million prokaryotic species on Earth; this Formal Comment suggests instead that there are more likely hundreds of millions or billions of species, and that the majority of these are bacteria associated with insects and other animals.

The number of species on Earth is a fundamental number in science. Yet, estimates of global biodiversity have been highly uncertain. There are presently approximately 1.9 million described species [1]. Estimates of the actual number (both described and undescribed) have ranged from the low millions into the trillions [2,3]. Furthermore, described species richness [1] is dominated by animals (1.3 million; 68%), not bacteria (approximately 10,000 species; 0.5%). Larsen and colleagues [2] summarized evidence suggesting that the majority of species on Earth may be bacteria associated with insect hosts and that bacterial richness may push global biodiversity into the hundreds of millions of species or even low billions.Louca and colleagues [4] (LEA hereafter) have claimed instead that there are only 40,100 host-associated bacterial species among all animal species and 0.8 to 1.6 million prokaryotic species overall (see their “Author summary”). Strangely, they excluded bacterial species associated with animal hosts from their estimates of total prokaryotic diversity and justified this by claiming that the estimates of Larsen and colleagues [2] were “mathematically flawed.” Here, I examine their claims and present new estimates of global biodiversity.Remarkably, all projections by LEA for host-associated bacterial richness were based on an estimate from one ant genus (Cephalotes), an estimate that is demonstrably incorrect by orders of magnitude (S1 Text). Without examining the underlying data [5], LEA estimated only 40 bacterial species among all 130 ant species in this genus. Yet, simply counting the bacterial species among the 25 sampled ant species in that genus reveals 616 unique bacterial species, of which 539 appear to be unique to the genus and 369 each unique to a single ant species (using the standard 97% cutoff for 16S divergence and data from [5]). Thus, there were >500 bacterial species among 25 ant species, not 40 bacterial species among 130 ant species. This mistake was further exacerbated by inexplicably ignoring data from the other 2 insect genera analyzed by Larsen and colleagues [2], thus maximizing the impact of their incorrect estimate for this genus.Their overall estimate of bacterial richness was also strongly influenced by their questionable assumption that all animal genera can share bacterial species (i.e., reducing their estimate of 3 million host-associated bacterial species to only 40,100). They assumed “a conservative overlap of only 0.1% between any two randomly chosen genera” for the number of bacterial species shared between animal genera. No justification was given for this value of 0.1%, nor were any alternative values explored. Furthermore, they implicitly assumed that any bacterial species can be shared between any pair of animal genera, regardless of their phylogeny, habitat, or geographic range. So, for example, a bacterial species that is a gut endosymbiont of a terrestrial herbivorous insect species endemic to Madagascar could somehow be shared with a deep-sea worm in the northern Pacific Ocean. This is ridiculous: there must be a reason why bacterial species are shared among host species and genera (e.g., shared phylogeny, location, diet). For example, broad-scale studies show that sharing of bacteria among insect hosts is associated with both host phylogeny and diet [6].LEA stated “it is known that substantial overlap exists between the microbiota of different host genera and even of distantly related animal taxa.” However, they provided no numbers to justify this “substantial overlap.” In fact, none of the papers they cited as supporting this assumption actually do (S2 Text). For example, one study [7] found 5 bacterial species shared among 5 insect genera utilizing the same type of host plant (cycads). However, LEA do not mention that this study found 1,789 unique bacterial species among just these 5 insect species (or 177 after filtering). This seems inconsistent with their estimate of only 40,100 bacterial species across all animals. In summary, rather than estimating the overlap of bacterial species among host genera, LEA simply made a number up and combined this with unrealistic, unsupported assumptions about overlap. If LEA had considered Cephalotes (which all their estimates were based on), a survey of this genus and related genera [5] found 1,019 bacterial species, with only 77 of the 616 bacterial species in Cephalotes shared with other sampled genera, and the sharing of bacterial species among hosts strongly related to host phylogeny.Numerous surveys of bacterial diversity in insects strongly suggest that there are far more than 40,100 bacterial species among all animals (8] found roughly twice as many bacterial species as those of approximately 30 insect species [5,9], and the study of 218 insect species [6] found >3.5 times as many as the study of 62 insect species. The simple fact that a study found 9,301 bacterial species among only 218 sampled insect species strongly suggests that there are more than 40,100 bacteria among all animals.Table 1Surveys of bacterial diversity among insect species.LEA incorrectly estimated that a genus of 130 ant species (Cephalotes) hosts only 40 bacterial species and subsequently assumed that all animal genera have the same low number of bacterial species. These broad surveys of bacterial species among insects suggest that many insects (including Cephalotes) host much larger numbers of bacterial species.

Genome-wide analysis of lipoxygenase gene family in Arabidopsis and rice

The enzymes called lipoxygenases (LOXs) can dioxygenate unsaturated fatty acids, which leads to lipoperoxidation of biological membranes. This process causes synthesis of signaling molecules and also leads to changes in cellular metabolism. LOXs are known to be involved in apoptotic (programmed cell death) pathway, and biotic and abiotic stress responses in plants. Here, the members of LOX gene family in Arabidopsis and rice are identified. The Arabidopsis and rice genomes encode 6 and 14 LOX proteins, respectively, and interestingly, with more LOX genes in rice. The rice LOXs are validated based on protein alignment studies. This is the first report wherein LOXs are identified in rice which may allow better understanding the initiation, progression and effects of apoptosis, and responses to bitoic and abiotic stresses and signaling cascades in plants.Key words: apoptosis, biotic and abiotic stresses, genomics, jasmonic acid, lipidsLipoxygenases (linoleate:oxygen oxidoreductase, EC 1.13.11.-; LOXs) catalyze the conversion of polyunsaturated fatty acids (lipids) into conjugated hydroperoxides. This process is called hydroperoxidation of lipids. LOXs are monomeric, non-heme and non-sulfur, but iron-containing dioxygenases widely expressed in fungi, animal and plant cells, and are known to be absent in prokaryotes. However, a recent finding suggests the existence of LOX-related genomic sequences in bacteria but not in archaea.¹ The inflammatory conditions in mammals like bronchial asthama, psoriasis and arthritis are a result of LOXs reactions.² Further, several clinical conditions like HIV-1 infection,³ disease of kidneys due to the activation of 5-lipoxygenase,^4,5 aging of the brain due to neuronal 5-lipoxygenase⁶ and atherosclerosis⁷ are mediated by LOXs. In plants, LOXs are involved in response to biotic and abiotic stresses.⁸ They are involved in germination⁹ and also in traumatin and jasmonic acid biochemical pathways.^10,11 Studies on LOX in rice are conducted to develop novel strategies against insect pests¹² in response to wounding and insect attack,¹³ and on rice bran extracts as functional foods and dietary supplements for control of inflammation and joint health.¹⁴ In Arabidopsis, LOXs are studied in response to natural and stress-induced senescence,¹⁵ transition to flowering,¹⁶ regulation of lateral root development and defense response.¹⁷The arachidonic, linoleic and linolenic acids can act as substrates for different LOX isozymes. A hydroperoxy group is added at carbons 5, 12 or 15, when arachidonic acid is the substrate, and so the LOXs are designated as 5-, 12- or 15-lipoxygenases. Sequences are available in the database for plant lipoxygenases (EC:1.13.11.12), mammalian arachidonate 5-lipoxygenase (EC:1.13.11.34), mammalian arachidonate 12-lipoxygenase (EC:1.13.11.31) and mammalian erythroid cell-specific 15-lipoxygenase (EC:1.13.11.33). The prototype member for LOX family, LOX-1 of Glycine max L. (soybean) is a 15-lipoxygenase. The LOX isoforms of soybean (LOX-1, LOX-2, LOX-3a and LOX-3b) are the most characterized of plant LOXs.¹⁸ In addition, five vegetative LOXs (VLX-A, -B, -C, -D, -E) are detected in soybean leaves.¹⁹ The 3-dimensional structure of soybean LOX-1 has been determined.^20,21 LOX-1 was shown to be made of two domains, the N-terminal domain-I which forms a β-barrel of 146 residues, and a C-terminal domain-II of bundle of helices of 693 residues²¹ (Fig. 1). The iron atom was shown to be at the centre of domain-II bound by four coordinating ligands, of which three are histidine residues.²²Open in a separate windowFigure 1Three-dimensional structure of soybean lipoxygenase L-1. The domain I (N-terminal) and domain II (C-terminal) are indicated. The catalytic iron atom is embedded in domain II (PDB ID-1YGE).²¹This article describes identification of LOX genes in Arabidopsis and rice. The Arabidopsis genome encodes for six LOX proteins²³ (www.arabidopsis.org) (

Aluminum induced proteome changes in tomato cotyledons

Cotyledons of tomato seedlings that germinated in a 20 µM AlK(SO₄)₂ solution remained chlorotic while those germinated in an aluminum free medium were normal (green) in color. Previously, we have reported the effect of aluminum toxicity on root proteome in tomato seedlings (Zhou et al.¹). Two dimensional DIGE protein analysis demonstrated that Al stress affected three major processes in the chlorotic cotyledons: antioxidant and detoxification metabolism (induced), glyoxylate and glycolytic processes (enhanced), and the photosynthetic and carbon fixation machinery (suppressed).Key words: aluminum, cotyledons, proteome, tomatoDifferent biochemical processes occur depending on the developmental stages of cotyledons. During early seed germination, before the greening of the cotyledons, glyoxysomes enzymes are very active. Fatty acids are converted to glucose via the gluconeogenesis pathway.^2,3 In greening cotyledons, chloroplast proteins for photosynthesis and leaf peroxisomal enzymes in the glycolate pathway for photorespiration are metabolized.^2–4 Enzymes involved in regulatory mechanisms such as protein kinases, protein phosphatases, and mitochondrial enzymes are highly expressed.^3,5,6The chlorotic cotyledons are similar to other chlorotic counterparts in that both contains lower levels of chlorophyll, thus the photosynthetic activities are not as active. In order to understand the impact of Al on tomato cotyledon development, a comparative proteome analysis was performed using 2D-DIGE following the as previously described procedure.¹ Some proteins accumulated differentially in Al-treated (chlorotic) and untreated cotyledons (Fig. 1). Mass spectrometry of tryptic digestion fragments of the proteins followed by database search has identified some of the differentially expressed proteins (Open in a separate windowFigure 1Image of protein spots generated by Samspot analysis of Al treated and untreated tomato cotyledons proteomes separated on 2D-DIGE.

Table 1

Proteins identified from tomato cotyledons of seeds germinating in Al-solution

Natural Infection of Burkholderia pseudomallei in an Imported Pigtail Macaque (Macaca nemestrina) and Management of the Exposed Colony

Identification of the select agent Burkholderia pseudomallei in macaques imported into the United States is rare. A purpose-bred, 4.5-y-old pigtail macaque (Macaca nemestrina) imported from Southeast Asia was received from a commercial vendor at our facility in March 2012. After the initial acclimation period of 5 to 7 d, physical examination of the macaque revealed a subcutaneous abscess that surrounded the right stifle joint. The wound was treated and resolved over 3 mo. In August 2012, 2 mo after the stifle joint wound resolved, the macaque exhibited neurologic clinical signs. Postmortem microbiologic analysis revealed that the macaque was infected with B. pseudomallei. This case report describes the clinical evaluation of a B. pseudomallei-infected macaque, management and care of the potentially exposed colony of animals, and protocols established for the animal care staff that worked with the infected macaque and potentially exposed colony. This article also provides relevant information on addressing matters related to regulatory issues and risk management of potentially exposed animals and animal care staff.Abbreviations: CDC, Centers for Disease Control and Prevention; IHA, indirect hemagglutination assay; PEP, postexposure prophylacticBurkholderia pseudomallei, formerly known as Pseudomonas pseudomallei, is a gram-negative, aerobic, bipolar, motile, rod-shaped bacterium. B. pseudomallei infections (melioidosis) can be severe and even fatal in both humans and animals. This environmental saprophyte is endemic to Southeast Asia and northern Australia, but it has also been found in other tropical and subtropical areas of the world.^7,22,32,42 The bacterium is usually found in soil and water in endemic areas and is transmitted to humans and animals primarily through percutaneous inoculation, ingestion, or inhalation of a contaminated source.^{8, 22,28,32,42} Human-to-human, animal-to-animal, and animal-to-human spread are rare.^8,32 In December 2012, the National Select Agent Registry designated B. pseudomallei as a Tier 1 overlap select agent.³⁹ Organisms classified as Tier 1 agents present the highest risk of deliberate misuse, with the most significant potential for mass casualties or devastating effects to the economy, critical infrastructure, or public confidence. Select agents with this status have the potential to pose a severe threat to human and animal health or safety or the ability to be used as a biologic weapon.³⁹Melioidosis in humans can be challenging to diagnose and treat because the organism can remain latent for years and is resistant to many antibiotics.^12,37,41 B. pseudomallei can survive in phagocytic cells, a phenomenon that may be associated with latent infections.^19,38 The incubation period in naturally infected animals ranges from 1 d to many years, but symptoms typically appear 2 to 4 wk after exposure.^13,17,35,38 Disease generally presents in 1 of 2 forms: localized infection or septicemia.²² Multiple methods are used to diagnose melioidosis, including immunofluorescence, serology, and PCR analysis, but isolation of the bacteria from blood, urine, sputum, throat swabs, abscesses, skin, or tissue lesions remains the ‘gold standard.’^9,22,40,42 The prognosis varies based on presentation, time to diagnosis, initiation of appropriate antimicrobial treatment, and underlying comorbidities.^7,28,42 Currently, there is no licensed vaccine to prevent melioidosis.There are several published reports of naturally occurring melioidosis in a variety of nonhuman primates (NHP; 2,10,13,17,25,30,31,35 The first reported case of melioidosis in monkeys was recorded in 1932, and the first published case in a macaque species was in 1966.³⁰ In the United States, there have only been 7 documented cases of NHP with B. pseudomallei infection.^2,13,17 All of these cases occurred prior to the classification of B. pseudomallei as a select agent. Clinical signs in NHP range from subclinical or subacute illness to acute septicemia, localized infection, and chronic infection. NHP with melioidosis can be asymptomatic or exhibit clinical signs such as anorexia, wasting, purulent drainage, subcutaneous abscesses, and other soft tissue lesions. Lymphadenitis, lameness, osteomyelitis, paralysis and other CNS signs have also been reported.^{2,7,10,22,28,32} In comparison, human''s clinical signs range from abscesses, skin ulceration, fever, headache, joint pain, and muscle tenderness to abdominal pain, anorexia, respiratory distress, seizures, and septicemia.^7,9,21,22

Table 1.

Summary of reported cases of naturally occurring Burkholderia pseudomalleiinfections in nonhuman primates

Association of Campylobacter jejuni Cj0859c Gene (fspA) Variants with Different C. jejuni Multilocus Sequence Types

Cj0859c variants fspA1 and fspA2 from 669 human, poultry, and bovine Campylobacter jejuni strains were associated with certain hosts and multilocus sequence typing (MLST) types. Among the human and poultry strains, fspA1 was significantly (P < 0.001) more common than fspA2. FspA2 amino acid sequences were the most diverse and were often truncated.Campylobacter jejuni is the leading cause of bacterial gastroenteritis worldwide and responsible for more than 90% of Campylobacter infections (7). Case-control studies have identified consumption or handling of raw and undercooked poultry meat, drinking unpasteurized milk, and swimming in natural water sources as risk factors for acquiring domestic campylobacteriosis in Finland (7, 9). Multilocus sequence typing (MLST) has been employed to study the molecular epidemiology of Campylobacter (4) and can contribute to virulotyping when combined with known virulence factors (5). FspA proteins are small, acidic, flagellum-secreted nonflagellar proteins of C. jejuni that are encoded by Cj0859c, which is expressed by a σ²⁸ promoter (8). Both FspA1 and FspA2 were shown to be immunogenic in mice and protected against disease after challenge with a homologous strain (1). However, FspA1 also protected against illness after challenge with a heterologous strain, whereas FspA2 failed to do the same at a significant level. Neither FspA1 nor FspA2 protected against colonization (1). On the other hand, FspA2 has been shown to induce apoptosis in INT407 cells, a feature not exhibited by FspA1 (8). Therefore, our aim was to study the distributions of fspA1 and fspA2 among MLST types of Finnish human, chicken, and bovine strains.In total, 367 human isolates, 183 chicken isolates, and 119 bovine isolates (n = 669) were included in the analyses (3). PCR primers for Cj0859c were used as described previously (8). Primer pgo6.13 (5′-TTGTTGCAGTTCCAGCATCGGT-3′) was designed to sequence fspA1. Fisher''s exact test or a chi-square test was used to assess the associations between sequence types (STs) and Cj0859c. The SignalP 3.0 server was used for prediction of signal peptides (2).The fspA1 and fspA2 variants were found in 62.6% and 37.4% of the strains, respectively. In 0.3% of the strains, neither isoform was found. Among the human and chicken strains, fspA1 was significantly more common, whereas fspA2 was significantly more frequent among the bovine isolates (Table ​(Table1).1). Among the MLST clonal complexes (CCs), fspA1 was associated with the ST-22, ST-45, ST-283, and ST-677 CCs and fspA2 was associated with the ST-21, ST-52, ST-61, ST-206, ST-692, and ST-1332 CCs and ST-58, ST-475, and ST-4001. Although strong CC associations of fspA1 and fspA2 were found, the ST-48 complex showed a heterogeneous distribution of fspA1 and fspA2. Most isolates carried fspA2, and ST-475 was associated with fspA2. On the contrary, ST-48 commonly carried fspA1 (Table ​(Table1).1). In our previous studies, ST-48 was found in human isolates only (6), while ST-475 was found in both human and bovine isolates (3, 6). The strict host associations and striking difference between fspA variants in human ST-48 isolates and human/bovine ST-475 isolates suggest that fspA could be important in host adaptation.

TABLE 1.

Percent distributions of fspA1 and fspA2 variants among 669 human, poultry, and bovine Campylobacter jejuni strains and their associations with hosts, STs, and CCs

Detection and Quantification of the Coral Pathogen Vibrio coralliilyticus by Real-Time PCR with TaqMan Fluorescent Probes

A real-time quantitative PCR-based detection assay targeting the dnaJ gene (encoding heat shock protein 40) of the coral pathogen Vibrio coralliilyticus was developed. The assay is sensitive, detecting as little as 1 CFU per ml in seawater and 10⁴ CFU per cm² of coral tissue. Moreover, inhibition by DNA and cells derived from bacteria other than V. coralliilyticus was minimal. This assay represents a novel approach to coral disease diagnosis that will advance the field of coral disease research.Vibrio coralliilyticus has recently emerged as a coral pathogen of concern on reefs throughout the Indo-Pacific. It was first implicated as the etiological agent responsible for bleaching and tissue lysis of the coral Pocillopora damicornis on Zanzibar reefs (2). More recently, V. coralliilyticus has been identified as the causative agent of white syndrome (WS) outbreaks on several Pacific reefs (14). WS is a collective term describing coral diseases characterized by a spreading band of tissue loss exposing white skeleton on Indo-Pacific scleractinian corals (16). V. coralliilyticus is an emerging model pathogen for understanding the mechanisms linking bacterial infection and coral disease (13) and therefore provides an ideal model for the development of diagnostic assays to detect coral disease. Current coral disease diagnostic methods, which are based primarily upon field-based observations of macroscopic disease signs, often detect disease only at the latest stages of infection, when control measures are least effective. The development of diagnostic tools targeting pathogens underlying coral disease pathologies may provide early indications of infection, aid the identification of disease vectors and reservoirs, and assist managers in developing strategies to prevent the spread of coral disease outbreaks. In this paper, we describe the development and validation of a TaqMan-based real-time quantitative PCR (qPCR) assay that targets a segment of the V. coralliilyticus heat shock protein 40-encoding gene (dnaJ).Nucleotide sequences of the dnaJ gene were retrieved from relevant Vibrio species, including V. coralliilyticus (LMG 20984), using the National Center for Biotechnology Information''s (NCBI) Entrez Nucleotide Database search tool (http://www.ncbi.nlm.nih.gov/). Gene sequences of strains not available in public databases (V. coralliilyticus strains LMG 21348, LMG 21349, LMG 21350, LMG 10953, LMG 20538, LMG 23696, LMG 23691, LMG 23693, LMG 23692, and LMG 23694) were obtained through extraction of total DNA using a Promega Wizard Prep DNA Purification Kit (Promega, Sydney, Australia), PCR amplification, and sequencing using primers and thermal cycling parameters described by Nhung et al. (8). A 128-bp region (nucleotides 363 to 490) containing high concentrations of single nucleotide polymorphisms (SNPs), which were conserved within V. coralliilyticus strains but differed from non-V. coralliilyticus strains, was identified, and oligonucleotide primers Vc_dnaJ_F1 (5′-CGG TTC GYG GTG TTT CAA AA-3′) and Vc_dnaJ_R1 (5′-AAC CTG ACC ATG ACC GTG ACA-3′) and a TaqMan probe, Vc_dnaJ_TMP (5′-6-FAM-CAG TGG CGC GAA G-MGBNFQ-3′; 6-FAM is 6-carboxyfluorescein and MGBNFQ is molecular groove binding nonfluorescent quencher), were designed to target this region. The qPCR assay was optimized and validated using DNA extracted from V. coralliilyticus isolates, nontarget Vibrio species, and other bacterial species grown in marine broth (MB) (Table ​(Table1),1), under the following optimal conditions: 1× TaqMan buffer A, 0.5 U of AmpliTaq Gold DNA polymerase, 200 μM deoxynucleotide triphosphates (with 400 μM dUTP replacing deoxythymidine triphosphate), 0.2 U of AmpErase uracil N-glycosylase (UNG), 3 mM MgCl₂, 0.6 μM each primer, 0.2 μM fluorophore-labeled TaqMan, 1 μl of template, and sterile MilliQ water for a total reaction volume to 20 μl. All assays were conducted on a RotoGene 300 (Corbett Research, Sydney, Australia) real-time analyzer with the following cycling parameters: 50°C for 120 s (UNG activation) and 95°C for 10 min (AmpliTaq Gold DNA polymerase activation), followed by 40 cycles of 95°C for 15 s (denaturation) and 60°C for 60 s (annealing/extension). During the annealing/extension phase of each thermal cycle, fluorescence was measured in the FAM channel (470-nm excitation and 510-nm detection).

TABLE 1.

Species, strain, and threshold cycle for all bacterial strains tested^a

Mouse Models of Osteoarthritis: A Summary of Models and Outcomes Assessment

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease. It has a substantial impact on patient quality of life and is a common cause of pain and mobility issues in older adults. The functional limitations, lack of curative treatments, and cost to society all demonstrate the need for translational and clinical research. The use of OA models in mice is important for achieving a better understanding of the disease. Models with clinical relevance are needed to achieve 2 main goals: to assess the impact of the OA disease (pain and function) and to study the efficacy of potential treatments. However, few OA models include practical strategies for functional assessment of the mice. OA signs in mice incorporate complex interrelations between pain and dysfunction. The current review provides a comprehensive compilation of mouse models of OA and animal evaluations that include static and dynamic clinical assessment of the mice, merging evaluation of pain and function by using automatic and noninvasive techniques. These new techniques allow simultaneous recording of spontaneous activity from thousands of home cages and also monitor environment conditions. Technologies such as videography and computational approaches can also be used to improve pain assessment in rodents but these new tools must first be validated experimentally. An example of a new tool is the digital ventilated cage, which is an automated home-cage monitor that records spontaneous activity in the cages.

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease.³⁶ Functional limitations, the absence of curative treatments, and the considerable cost to society result in a substantial impact on quality of life.⁷⁶ Historically, OA has been described as whole joint and whole peri-articular diseases and as a systemic comorbidity.^9,111 OA consists of a disruption of articular joint cartilage homeostasis leading to a catabolic pathway characterized by chondrocyte degeneration and destruction of the extracellular matrix (ECM). Low-grade chronic systemic inflammation is also actively involved in the process.^42,92 In clinical practice, mechanical pain, often accompanied by a functional decline, is the main reason for consultations. Recommendations to patients provide guidance for OA management.^{22, 33,49,86} Evidence-based consensus has led to a variety of pharmacologic and nonpharmacologic modalities that are intended to guide health care providers in managing symptomatic patients. Animal-based research is of tremendous importance for the study of early diagnosis and treatment, which are crucial to prevent the disease progression and provide better care to patients.The purpose of animal-based OA research is 2-fold: to assess the impact of the OA disease (pain and function) and to study the efficacy of a potential treatment.^18,67 OA model species include large animals such as the horse, goat, sheep, and dog, whose size and anatomy are expected to better reflect human joint conditions. However, small animals such as guinea pig, rabbit, mouse, and rat represent 77% of the species used.^1,87 In recent years, mice have become the most commonly used model for studying OA. Mice have several advantageous characteristics: a short development and life span, easy and low-cost breeding and maintenance, easy handling, small joints that allow histologic analysis of the whole joint,³² and the availability of genetically modified lines.¹⁰⁸ Standardized housing, genetically defined strains and SPF animals reduce the genetic and interindividual acquired variability. Mice are considered the best vertebrate model in terms of monitoring and controlling environmental conditions.^7,14,15,87 Mouse skeletal maturation is reached at 10 wk, which theoretically constitutes the minimal age at which mice should be entered into an OA study.^64,87,102 However, many studies violate this limit by testing mice at 8 wk of age.Available models for OA include the following (32,111 physical activity and exercise induced OA; noninvasive mechanical loading (repetitive mild loading and single-impact injury); and surgically induced (meniscectomy models or anterior cruciate ligament transection). The specific model used would be based on the goal of the study.⁷ For example, OA pathophysiology, OA progression, and OA therapies studies could use spontaneous, genetic, surgical, or noninvasive models. In addition, pain studies could use chemical models. Lastly, post-traumatic studies would use surgical or noninvasive models; the most frequently used method is currently destabilization of the medial meniscus,³² which involves transection of the medial meniscotibial ligament, thereby destabilizing the joint and causing instability-driven OA. An important caveat for mouse models is that the mouse and human knee differ in terms of joint size, joint biomechanics, and histologic characteristics (layers, cellularity),^32,64 and joint differences could confound clinical translation.¹⁰ Table 1. Mouse models of osteoarthritis.