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.

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

Evolutionary Strata in a Small Mating-Type-Specific Region of the Smut Fungus Microbotryum violaceum

DNA sequence analysis and genetic mapping of loci from mating-type-specific chromosomes of the smut fungus Microbotryum violaceum demonstrated that the nonrecombining mating-type-specific region in this species comprises ∼25% (∼1 Mb) of the chromosome length. Divergence between homologous mating-type-linked genes in this region varies between 0 and 8.6%, resembling the evolutionary strata of vertebrate and plant sex chromosomes.EVOLUTION of mating types or sex-determining systems often involves the suppression of recombination around the primary sex-determining or mating-type-determining locus. In animals and plants, it is often an entire or almost entire chromosome (Y or W in male or female heterogametic species, respectively) that ceases to recombine with its homologous (X or Z) chromosome (Charlesworth and Charlesworth 2000; Charlesworth 2008). Self-incompatibility loci in plants are also thought to be located in regions of suppressed recombination (Charlesworth et al. 2005; Kamau and Charlesworth 2005; Kamau et al. 2007; Li et al. 2007; Yang et al. 2007). Regardless of the phylogenetic position of a species, such nonrecombining regions are known to follow similar evolutionary trajectories. The nonrecombining region on the sex-specific chromosome expands in several steps, forming evolutionary strata—regions of different X/Y (or Z/W) divergence (Lahn and Page 1999; Handley et al. 2004; Sandstedt and Tucker 2004; Nicolas et al. 2005)—and genes in the nonrecombining regions gradually accumulate deleterious mutations that eventually render them dysfunctional (Charlesworth and Charlesworth 2005; Charlesworth 2008).Fungal mating-type systems are very diverse, with the number of mating types varying from two to several hundred (Casselton 2002). Like sex chromosomes in several animals and plants, suppressed recombination has evolved in regions near fungal mating-type loci, including in Ustilago hordei (Lee et al. 1999), Cryptococcus neoformans (Lengeler et al. 2002), and Neurospora tetrasperma (Menkis et al. 2008). These species have two mating types, but no morphologically distinct sexes. The mating-type locus (the region of suppressed recombination) of C. neoformans is small (∼100 kb) compared with known sex chromosomes and contains only ∼20 genes that, unlike many sex chromosomes (Y or W chromosomes), show no obvious signs of genetic degeneration (Lengeler et al. 2002; Fraser et al. 2004). Judging from the divergence between the homologous genes on the two mating-type-specific chromosomes, C. neoformans started to evolve sex chromosomes a long time ago because silent divergence between the two mating types in the most ancient region exceeds 100% (Fraser et al. 2004). Genes in the younger mating-type-specific region are much less diverged between the two sex chromosomes, suggesting that the evolution of the sex locus in C. neoformans might have proceeded through several steps. The nonrecombining region around the mating-type locus of N. tetrasperma is much larger than in C. neoformans (at least 6.6 Mb), and silent divergence between homologous genes on the mating-type-specific chromosomes ranges from zero to 9%, demonstrating that these mating-type-specific chromosomes evolved recently (Menkis et al. 2008).M. violaceum, which causes anther smut disease in Silene latifolia and other species in the family Caryophyllaceae, has two mating types, A1 and A2 (reviewed by Giraud et al. 2008), which are determined by the presence of mating-type-specific chromosomes (hereafter A1 and A2 chromosomes, or sex chromosomes) in the haploid stage of the life cycle (Hood 2002; Hood et al. 2004). The A1 and A2 chromosomes are distinguishable by size in pulsed-field electrophoresis, and it is possible to isolate individual chromosomes electrophoretically (Hood et al. 2004). Random fragments of A1 and A2 chromosomes have previously been isolated from mating-type-specific bands of pulsed-field separated chromosomes of M. violaceum (Hood et al. 2004). These fragments were assumed to be linked to mating type. The same method was used to isolate fragments of non-mating-type-specific chromosomes. On the basis of the analysis of their sequences, (Hood et al. 2004) proposed that mating-type-specific chromosomes in M. violaceum might be degenerate because they contained a lower proportion of protein-coding genes than other chromosomes. However, it was not determined whether the sequences isolated from the mating-type chromosomes originated from the mating-type-specific or from the recombining regions (Hood et al. 2004), and the relative sizes of these regions are not known for these M. violaceum chromosomes. We tested the mating-type specificity of 86 of these fragments and demonstrate that fewer than a quarter of these loci are located in the mating-type-specific region, suggesting that the nonrecombining region on the A1 and A2 chromosomes is quite small, while the rest of the chromosome probably recombines (like pseudoautosomal regions of sex chromosomes) and is therefore not expected to undergo genetic degeneration. Genetic mapping confirms the presence of two pseudoautosomal regions in the M. violaceum mating-type-specific chromosomes.As these chromosomes are mating type specific in the haploid stage of M. violaceum, mating-type-specific loci (or DNA fragments) can be identified by testing whether they are present exclusively in A1 or A2 haploid strains. We therefore prepared haploid A1 and A2 M. violaceum cultures from S. latifolia plants from two geographically remote locations (accessions Sl405 from Sweden and Sl127 from the French Pyrenees). Haploid sporidial cultures were isolated by a standard dilution method (Kaltz and Shykoff 1997; Oudemans and Alexander 1998). Mating types were determined by PCR amplification of each culture with primers designed for A1 and A2 pheromone receptor genes linked to A1 and A2 mating types (Yockteng et al. 2007). The primers were as follows: 5′-TGGCATCCCTCAATGTTTCC-3′ and 5′-CACCTTTTGATGAGAGGCCG-3′ for the A1 pheromone receptor (GenBank accession no. {"type":"entrez-nucleotide","attrs":{"text":"EF584742","term_id":"156254864","term_text":"EF584742"}}EF584742) and 5′-TGACGAGAGCATTCCTACCG-3′ and 5′-GAAGCGGAACTTGCCTTTCT-3′ for the A2 pheromone receptor (GenBank accession no. {"type":"entrez-nucleotide","attrs":{"text":"EF584741","term_id":"156254862","term_text":"EF584741"}}EF584741). Cultures with PCR product amplified only from an A1 or A2 pheromone receptor gene were selected for further use. The mating types of the cultures were verified by conjugating them in all combinations.The GenBank nucleotide database was searched using BLAST for sequences similar to those isolated by Hood et al. (2004). Sequences with similarity to transposable elements (TE) and other repeats were excluded. The resulting set of nonredundant sequences was used to design PCR primers for 98 fragments. Half of these were originally isolated from the A1 and half from the A2 chromosomes and are hereafter called A1-NNN or A2-NNN (where NNN is the locus number; supporting information, Table S1), which does not imply that these loci are A1 or A2 specific, but merely indicates that they were originally isolated from the A1 or A2 chromosomes. Amplification of these regions from new A1 and A2 M. violaceum cultures, independently isolated by ourselves, revealed that only 5 of the 49 loci isolated from the A1 chromosome are indeed A1 specific and only 6 of 49 isolated from the A2 chromosome are A2 specific. All other loci amplified from both A1 and A2 cultures. Figure 1 illustrates some of these results from the Swedish sample (Sl405).Open in a separate windowFigure 1.—Testing of mating-type specificity for loci isolated from A1 and A2 chromosomes. (a) PCR amplifications from haploid cultures from Sl405 using primers designed from six A1-originated loci. Loci in which a PCR product could be amplified only from A1 cultures (boxed) were classified as specific to mating type A1. (b) PCR tests of six A2-originated loci on the same set of haploids as in a. Loci in which a PCR product amplified only from A2 cultures (boxed) were classified as specific to mating type A2. Loci amplified from both A1 and A2 cultures are not mating type specific.The fragments that amplified from both A1 and A2 mating types may be in recombining regions, or they could be present in mating-type-specific regions on both A1 and A2 chromosomes. If they are in recombining regions, the A1- and A2-linked homologs should not be diverged from each other, but if they are in nonrecombining, mating-type-specific regions, the divergence of the A1- and A2-linked homologs should be roughly proportional to the time since recombination stopped in the region. We therefore sequenced and compared PCR fragments amplified from the two mating types of Sl405 or Sl127 cultures (GenBank accession nos. {"type":"entrez-nucleotide","attrs":{"text":"FI855822","term_id":"258612459","term_text":"FI855822"}}FI855822–{"type":"entrez-nucleotide","attrs":{"text":"FI856001","term_id":"258612570","term_text":"FI856001"}}FI856001). Sequencing of PCR products showed that 12 (4 A1 and 8 A2) loci have more than one copy, and they were excluded from further analysis. Sequences of 61 loci were identical between the A1 and A2 strains, and four loci demonstrated low total divergence (0.24–0.61%) between the two mating types (otintseva and D. Filatov, unpublished results). Thus, these loci might be located in the recombining part of the mating-type-specific chromosomes. Ten of 75 loci that amplified in both mating types demonstrated multiple polymorphisms fixed between the mating types rather than between the locations. Given that the strains that we used in the analysis originated from two geographically distant locations, it is highly unlikely that multiple polymorphisms distinguishing the A1 and A2 sequences arose purely by chance; thus, these loci are probably located in the nonrecombining mating-type-specific region of the M. violaceum A1 and A2 chromosomes.

TABLE 1

Loci from mating-type-specific chromosomes of M. violaceum used for PCR analysis and genetic map construction

		With nonzero A1/A2 divergenceb
Loci	Mating type specific	<1%	>1%	With zero A1/A2 divergenceb	Total
A1a	5	2 (1)	3 (3)	35 (3)	45 (7)
A2a	6	2 (0)	7 (7)	26 (3)	41 (10)
Subtotal		4 (1)	10 (10)
Total	11	14 (11)		61 (6)	86 (17)

Open in a separate window^aA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.^bThe number of loci used for genetic map construction is in parentheses.To confirm the mating-type-specific or pseudoautosomal locations of the loci with and without A1/A2 divergence, we conducted genetic mapping in a family of 99 individuals, 50 of which were of mating type A1 and 49 of mating type A2. The family was generated by a cross between A1 and A2 M. violaceum strains from S. latifolia accessions Sl405 (Sweden) and Sl127 (France), respectively. The choice of strains from geographically distant locations was motivated by the hope of maximizing the number of DNA sequence differences between them that can be used as molecular genetic markers in segregation analysis. We inoculated S. latifolia seedlings with sporidial cultures of both mating types. For inoculation, petri dishes with 12-day-old seedlings of S. latifolia were flooded with 2.5 ml of inoculum suspension. Inoculum suspension consisted of equal volumes of the A1 and A2 sporidial cultures that were mixed and conjugated overnight at 14° under rotation (Biere and Honders 1996; Van Putten et al. 2003). Seedlings were potted 3 days after inoculation. Two months later, teliospores were collected from the flowers of the infected plant and grown in petri dishes on 3.6% potato dextrose agar medium. Haploid sporidia formed after meiosis were isolated and grown as separate cultures for DNA extraction. The mating types of single sporidia cultures were identified as described above. The loci analyzed in the segregation analysis were sequenced in the two parental haploid strains and in 99 (50 A1 and 49 A2) haploid strains that were generated in the cross. Single nucleotide differences between the parental strains were used as molecular genetic markers for segregation analysis in the progeny. The genetic map was constructed using MAPMAKER/EXP v3.0 (Lincoln et al. 1992) and MapDisto v1.7 (http://mapdisto.free.fr/).The resulting genetic map is shown in Figure 2. As expected, no recombination was observed between the 10 loci with diverged A1- and A2-linked copies. In addition, one marker with no A1/A2 divergence, A2-397, was also completely linked to the loci with significant A1/A2 divergence. This locus either may be very tightly linked to the nonrecombining mating-type-specific region or may have been added to that region more recently than the loci that had already accumulated some divergence between the alleles in the two mating types. The mating-type-specific pheromone receptor locus (Devier et al. 2009) and 11 mating-type-specific loci are also located in this nonrecombining region (Figure 2). Interestingly, the cluster of nonrecombining markers is flanked on both sides with markers that recombine in meiosis, demonstrating that there are pseudoautosomal regions on both ends of the mating-type-specific chromosomes.Open in a separate windowFigure 2.—Genetic map of the mating-type-determining chromosome in M. violaceum. Genetic distance (in centimorgans) and the relative positions of the markers are shown to the left and the right of the chromosome, respectively. The position of the nonrecombining region corresponds to the cluster of linked markers shown on the right of the figure. Total A1/A2 divergence is shown in parentheses. Eleven mating-type-specific markers (for which sequences are available from only one mating type), located in the nonrecombining mating-type-specific region, are not shown.Our results demonstrate that although the loci reported by Hood et al. (2004) were isolated from the A1 and A2 chromosomes, most of these loci are not located in the nonrecombining mating-type-specific regions. In fact, the nonrecombining region might be relatively small: of 86 tested fragments, only 21 appeared to be either mating type specific or linked to the mating-type locus. Assuming that these loci represent a random set of DNA fragments isolated from the A1 and A2 chromosomes, it is possible to estimate the size of the nonrecombining region using the binomial distribution: the nonrecombining region is expected to be 24.4% (95% CI: 16.7–33.6%) of the chromosome length. As the sizes of the A1 and A2 chromosomes are ∼3.4 and 4.2 Mb long (Hood 2002; Hood et al. 2004), the nonrecombining region might be ∼1 Mb long.Interestingly, total A1/A2 divergence for the 11 loci with A1- and A2-linked copies mapped to the nonrecombining region varied from 0% to 8.6% (Figure 2). In addition, 11 loci amplified from only one mating type. These genes could represent degenerated genes, some of which degenerated in A1 strains, and some in A2 strains. Alternatively, they might be highly diverged genes, such that the PCR primers amplify only one allele, and not the other. Variation in divergence may be the result of the stepwise cessation of recombination between the A1 and A2 chromosomes in M. violaceum, resembling the evolutionary strata reported for human, chicken, and white campion sex chromosomes (Lahn and Page 1999; Handley et al. 2004; Bergero et al. 2007). However, only the differences between the most and the least diverged loci are statistically significant (Devier et al. 2009), the M. violaceum mating-type region has at least three strata: one oldest stratum, including the pheromone receptor locus; a younger stratum with ∼5–9% A1/A2 divergence; and the youngest stratum with 1–4% divergence between the two mating types. There may also be an additional very recently evolved stratum containing the locus named A2-397, which is also present in all A1 strains tested, with no fixed differences between the A1 and A2 strains (No. of sites analyzedWithin A1

---

Within A2

---

Fixed differences between A1 and A2A1/A2 divergence (%)Loci^aS^b totalSπ (%)^cSπ (%)^cA1/A2 divergence <1%A1-23645630020.4410.44A1-0456544000040.61A2-568413220.4820.4800.24A2-411480210.210010.31A1/A2 divergence >1%A1-2176679000091.35A1-12856990010.1881.49A1-199618130010.16122.02A2-4223449000092.62A2-516470140000142.98A2-404508200030.59173.64A2-4355062220.3920.39183.95A2-4734572310.2210.22214.81A2-4573031710.3300165.54A2-5755034750.9930.59398.55Open in a separate window^aA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.^bS, number of polymorphic sites.^cπ (%), average number of differences per 100 nucleotides.

TABLE 3

P-values for the 2 × 2 G-tests for significance of differences in A1/A2 divergence between the loci in the nonrecombining region

A Problem With the Correlation Coefficient as a Measure of Gene Expression Divergence

The correlation coefficient is commonly used as a measure of the divergence of gene expression profiles between different species. Here we point out a potential problem with this statistic: if measurement error is large relative to the differences in expression, the correlation coefficient will tend to show high divergence for genes that have relatively uniform levels of expression across tissues or time points. We show that genes with a conserved uniform pattern of expression have significantly higher levels of expression divergence, when measured using the correlation coefficient, than other genes, in a data set from mouse, rat, and human. We also show that the Euclidean distance yields low estimates of expression divergence for genes with a conserved uniform pattern of expression.IT is now possible to measure the expression levels of thousands of genes in multiple tissues at multiple times. This has led to investigations into the evolution of gene expression and how the pattern of expression changes on a genomic scale. In some analyses, the evolution of expression is considered only within one tissue, but in many studies the evolution across multiple tissues is investigated. In this latter case, the evolution of an expression profile—a vector of expression levels of a gene across several tissues—is considered.Several different statistics have been proposed to measure the divergence between gene expression profiles. The two most popular measures are the Euclidean distance (Jordan et al. 2005; Kim et al. 2006; Yanai et al. 2006; Urrutia et al. 2008) and Pearson''s correlation coefficient (Makova and Li 2003; Huminiecki and Wolfe 2004; Yang et al. 2005; Kim et al. 2006; Liao and Zhang 2006a,b; Xing et al. 2007; Urrutia et al. 2008). The correlation coefficient is often subtracted from one, so that the statistic varies from zero, when there has been no expression divergence, to a maximum of two; we refer to this statistic as the Pearson distance. Here we describe a significant shortcoming of the Pearson distance that is not shared by the Euclidean distance.To investigate properties of these two measures of expression divergence, we compiled a data set of 2859 orthologous genes from human, mouse, and rat for which we had microarray expression data from nine homologous tissues: bone marrow, heart, kidney, large intestine, pituitary, skeletal muscle, small intestine, spleen, and thymus). The expression data for rat came from Walker et al. (2004), the mouse data from Su et al. (2004), and the human data from Ge et al. (2005). Each tissue experiment had two replicates in mouse, a varying number of replicates in rat, and one in humans; some genes were also matched by multiple probe sets. To obtain an average across experiments and probe sets we processed the data as follows:

1. Raw CEL files of gene expression levels were obtained from the NCBI Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/projects/geo/).
2. The results from the mouse, rat, and human arrays were normalized separately using both the MAS5 (Affymetrix 2001) and the RMA algorithms (Irizarry et al. 2003) as implemented in Bioconductor (Gentleman et al. 2004). The results are qualitatively similar for the two normalization procedures, although recent analyses suggest that MAS5 normalization is generally better (Ploner et al. 2005; Lim et al. 2007).
3. The expression of each gene within a tissue was averaged across experiments and probe sets.

We computed expression distances (ED) between orthologous gene expression profiles, for each of the three species comparisons, rat–mouse, rat–human, and mouse–human, according to the two different distance metrics, the Euclidean distance and the Pearson distance:(1)Here x_ij is the expression level of the gene under consideration in species i in tissue j, and is the average expression level of the gene in species i across tissues. Expression levels are known in a total of k tissues.Because expression levels are measured on different microarray platforms in the three species, we compute relative abundance (RA) values, before calculating the Euclidean distance (Liao and Zhang 2006a). The RA is the expression of a gene in a particular tissue divided by the sum of the expression values of that gene across all tissues. We calculated RA values to remove “probe” effects (the tendency for a gene to bind its probe set on one platform more efficiently than on another platform). Because of probe effects it is not easy to distinguish absolute changes in expression and differences in binding efficiency. Calculating RA values removes this problem from the Euclidean distance. Pearson''s distance does not change under such a rescaling and so this is unnecessary.In some analyses the logarithm of the expression or RA values are used (e.g., Makova and Li 2003; Kim et al. 2006; Xing et al. 2007), and in others the expression values are used without this transformation (e.g., Huminiecki and Wolfe 2004; Jordan et al. 2005; Yang et al. 2005; Liao and Zhang 2006a,b; Yanai et al. 2006; Urrutia et al. 2008). We calculated both the Pearson and the Euclidean distances on log-transformed and untransformed expression values. The results are qualitatively similar so here we present only the results obtained using the logarithm of the expression or RA values.It is natural to expect the two measures of expression divergence to be positively correlated with one another; however, the Euclidean and Pearson distances are almost completely uncorrelated (MAS5 normalization, mouse–rat correlation coefficient = 0.06, human–rat r = 0.13, human–mouse r = 0.10; RMA normalization, mouse–rat correlation coefficient = −0.12, human–rat r = −0.00, human–mouse r = −0.08; Figure 1). This could, plausibly, be because the two statistics measure different aspects of divergence. However, irrespective of this, there is a potential problem associated with the Pearson distance. Imagine that we have a gene that is expressed at identical levels in all tissues in two species (i.e., expression levels are uniform between tissues and also between species). We quite reasonably assume that measured expression levels contain noise. Thus each measured expression level (x_ij) is the sum of the (assumed) uniform expression level and an independent random number representing noise. In this case there is no real divergence in the expression profile between the species. However, the two measures of divergence may differ greatly in this case. The Euclidean distance reflects only the noise present in the data and hence will be small if the noise is small. By contrast, the Pearson distance will have a value close to 1 since the second term in PeaD in Equation 1 will be close to zero, reflecting the fact that the noise components of different expression levels are independent. Thus the Pearson distance will give the impression that expression divergence is great, but all this apparent divergence is noise. This will be a problem with Pearson''s distance whenever measurement error is of the same magnitude as the differences in expression between tissues. This will therefore tend to be a problem for lowly expressed genes, where measurement error can be large relative to the true value.Open in a separate windowFigure 1.—The correlation between the Euclidean and Pearson distances for (a) mouse–rat, (b) human–rat, and (c) human–mouse. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.The above example is unrealistic because real gene expression profiles are rarely perfectly uniform. To investigate whether this shortcoming of the Pearson distance is a problem in real data sets, we determined genes with a relatively uniform pattern of expression in all three species considered above. To do this we computed the entropy of a gene''s expression, which is a measure of uniformity in expression across tissues (Schug et al. 2005): the higher the value of the entropy, the more uniform is the expression. We calculated the entropy for each gene in each of the three species, averaged these across species, and then took those genes in the upper quartile of mean entropy values as a data set of genes with a relatively conserved pattern of uniform expression.It is natural to expect those genes with a conserved uniform pattern of expression to have relatively low expression divergence; however, on average these genes have significantly higher Pearson distances than other genes (Figure 2; supporting information, Figure S1 and Figure S2). By contrast, the Euclidean distance shows the pattern one would anticipate; all of the conserved uniform genes have low expression divergence. It therefore seems likely that the Pearson distance is sensitive to measurement error and hence may not be a good measure of expression divergence.Open in a separate windowFigure 2.—The distribution of expression divergence values for those genes with a uniform pattern of expression that is conserved across species vs. the distribution for all genes for (a) Pearson and (b) Euclidean distances for mouse–rat. We present similar values for human–mouse and human–rat in Figure S1 and Figure S2. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.

TABLE 1

The median expression divergence for genes that have a conserved uniform pattern of expression (upper quartile of mean entropy values) vs. all other genes

Safety and feasibility of umbilical cord mesenchymal stem cells in treatment-refractory systemic lupus erythematosus nephritis: time for a double-blind placebo-controlled trial to determine efficacy

As translational clinical researchers familiar with the risk-benefit of hematopoietic stem cell transplantation in autoimmune diseases, we are intrigued by the recent report of umbilical cord mesenchymal stem cell (UC-MSC) transplantation in treatment-refractory systemic lupus erythematosus nephritis by Wang and colleagues. They report the results of an open-label single-arm multicenter phase I/II study. This stimulated us to examine whether collective data from this group provide sufficient evidence for the feasibility, safety, dose rationale, and potential efficacy of UC-MSCs to conduct a randomized controlled trial in such patients. Results, though confounded by variable baseline prednisone and immuno-suppressive treatment, appear to indicate near-term response rates of approximately 50%, which are comparable to those seen with hematopoietic stem cell transplantation but with less morbidity and mortality.Wang and colleagues have been in the forefront of evaluating the potential for mesenchymal stem cells (MSCs) to treat systemic lupus erythematosus (SLE), based on studies in murine autoimmune disease models, demonstrating immunomodulatory properties of MSCs [1]. We have reviewed the additional reports published in peer-reviewed journals from this group [2–5], together with two protocols available on ClinicalTrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT00698191","term_id":"NCT00698191"}}NCT00698191 and {"type":"clinical-trial","attrs":{"text":"NCT01741857","term_id":"NCT01741857"}}NCT01741857) (Table Authors (date)ClinicalTrials.gov protocol numberStudy design/duration of follow-upNumber of patientsMSC type/regimenConditioningSafety: deaths/serious infectionPD marker ^a EfficacySun et al. [2]NRSingle-arm/ median of 8.25 months (range of 3 to 28 months)16 (15 SLEN)UC, single infusionCYC 0.8 to 1.8 mg/kg intravenously, 2 to 4 days0/0Percentage of Treg cells increased at 3 months (P = 0.03)‘Decreasing SLEDAI and proteinuria^b in all patients’Liang et al. [3]{"type":"clinical-trial","attrs":{"text":"NCT 00698191","term_id":"NCT00698191"}}NCT 00698191Single-arm/17.2 ± 9.5 months15 SLENBM, single infusionIncluded in protocol, but NR0/0Percentage of Treg cells increased at 1 week and 3 and 6 months (P <0.05)‘Decreasing SLEDAI and proteinuria^b in all patients’Wang et al. [4]{"type":"clinical-trial","attrs":{"text":"NCT 00698191","term_id":"NCT00698191"}}NCT 00698191Unblinded-randomized, 2-arm/12 months58 (~88% SLEN)BM, UC, single versus 2× (7 days apart)CYC 10 mg/kg per day, day 4, 3, and 21/NRNDCR single: 16/30 (53%); double: 8/27 (29%)Wang et al. [5]NRSingle-arm/mean of 27 months87 (84% SLEN)BM, UC, single infusion, 18 patients retreated at relapseCYC 10 mg/ kg/day, day 4, 3, and 25/NRNDCR in 23/83, relapse 10/83Wang et al. [1] ^c{"type":"clinical-trial","attrs":{"text":"NCT 01741857","term_id":"NCT01741857"}}NCT 01741857Single-arm40 (38 SLEN)UC, 2× infusion, 7 days apart)No3/4NDMCR 13/PCR 11, 7 relapseOpen in a separate window ^aPharmacodynamic (PD) markers = increased peripheral blood regulatory T (Treg) cells, balanced T helper 1 (Th1)/Th2 cytokines; ^b [2] baseline (BL) proteinuria 3.1 (±1.2) g/day versus 3 months, 1.3 (±0.9) g/day (P <0.001, n = 15); [3] BL proteinuria 2.7 (±1.2) g/day versus 6 months, 0.9 (±0.8) g/day (P <0.01, n = 12); ^cprotocol described at ClinicalTrials.gov consistent with this study, but not explicitly noted in report. BM, bone marrow; CR, complete remission; CYC, cyclophosphamide; MCR, major clinical response; MSC, mesenchymal stem cell; ND, not done; NR, not reported; PCR, partial clinical response; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; SLEN, systemic lupus erythematosus nephritis; UC, umbilical cord.Protocol {"type":"clinical-trial","attrs":{"text":"NCT01741857","term_id":"NCT01741857"}}NCT01741857, first posted on 26 November 2012 and updated 1 November 2013, appears to be the protocol for the study recently published in Arthritis Research & Therapy [1]. The report largely reflects the protocol, although dose is not described and patient entry criteria required a dose of prednisone of more than 20 mg/day. In the report, only 10 of 40 patients were receiving prednisone of more than 20 mg/day, and one dose - 1 × 10⁶ cells per kg, infused twice 7 days apart - was evaluated. The umbilical cord mesenchymal stem cells (UC-MSCs) for infusion were centrally prepared by using a well-standardized, quality-controlled method, and infusions were well tolerated. One-year mortality (two patients with uncontrolled SLE) and morbidity (five serious infections) compare favorably to those seen with hematopoietic stem cell transplantation in patients with SLE [6].Previously reported studies by this group [2–5] evaluated stem cells derived from either bone marrow of healthy relatives or umbilical cords donated by healthy consenting mothers in patients with treatment-refractory SLE. Most patients had active SLE nephritis despite receiving prednisone of more than 20 mg and immuno-suppression with cyclophosphamide, mycophenylate mofetil, or leflunomide or a combination of these. In addition, they usually received ‘conditioning’ by cyclophosphamide 0.8 to 1.8 mg/kg per day for 3 days prior to transplant. In contrast, in the recently reported study [1], only UC-MSCs were transplanted, without ‘conditioning’, in patients with active treatment-refractory SLE nephritis receiving a more variable background of prednisone, often less than 20 mg/day, and immunosuppression. Although efficacy is difficult to evaluate because major clinical response (MCR) required that prednisone be tapered to less than 10 mg/day, while maintaining improvements in disease activity measured by British Isles Lupus Assessment Group and Systemic Lupus Erythematosus Disease Activity Index, conditioning is apparently not needed. However, because 18 of 40 patients were receiving prednisone of less than 20 mg/day at the start of the study, the efficacy criterion of ‘tapered’ prednisone is difficult to assess. Although 24 responses - 13 MCR and 11 partial clinical response (CR) - are reported, 6 MCR and 6 partial CR cannot be verified in the reported data, because prednisone tapering appears not to meet criteria for response. In addition, relapse occurred within a year in 6 patients, most receiving at least 20 mg prednisone at baseline. Nevertheless, there may be some evidence for potential efficacy or perhaps corticosteroid sparing, but dose regimen results are not sufficiently clear, or defined by relevant pharmacodynamic activity, to identify a specific UC-MSC dose to use for a randomized controlled trial (RCT).Review of previous reports may provide some additional insight (Table 4]. In the same study, patients were randomly assigned in an unblinded manner to receive a single infusion versus two infusions of MSC, 7 days apart; again, no significant difference in CR rate was observed. Of interest are two studies that reported (Table 2, 3]. No correlations with improvement in clinical status were reported, however.Overall, Wang and colleagues have demonstrated the feasibility of a multicenter trial, as they apparently recruited a sufficient number of patients in a reasonable period of time, and safety was acceptable. Apparently, conditioning pre-MSC dosing is not required, although this aspect of the treatment has not been studied in a controlled manner. It should be pointed out that the collective published results may reflect the fact that some of the same patients were reported in more than one study. For example, two publications [1, 4] refer to the same {"type":"clinical-trial","attrs":{"text":"NCT00698191","term_id":"NCT00698191"}}NCT00698191 protocol; thus, recruitment feasibility may be optimistic. The dose of stem cells, with biologic activity shown in two studies, may have been defined, although it is not absolutely clear (see above caveats). There are also some indications of an appropriate population, but again there seems to be some lack of clarity based on the recently published study [1] where the endpoints were not actually met.The lack of a well-rationalized dosing regimen, together with a lack of results from an appropriately designed, well-controlled study, makes it extremely difficult to develop a treatment approach with UC-MSCs. Nevertheless, we feel that these results should not be discarded without a proper RCT. Although there may be some challenges in designing a well-controlled, double-blind, placebo-controlled RCT in patients with prednisone-dependent active SLE nephritis, this should be attempted [7].     

Increased Crystalline Cellulose Activity via Combinations of Amino Acid Changes in the Family 9 Catalytic Domain and Family 3c Cellulose Binding Module of Thermobifida fusca Cel9A

Amino acid modifications of the Thermobifida fusca Cel9A-68 catalytic domain or carbohydrate binding module 3c (CBM3c) were combined to create enzymes with changed amino acids in both domains. Bacterial crystalline cellulose (BC) and swollen cellulose (SWC) assays of the expressed and purified enzymes showed that three combinations resulted in 150% and 200% increased activity, respectively, and also increased synergistic activity with other cellulases. Several other combinations resulted in drastically lowered activity, giving insight into the need for a balance between the binding in the catalytic cleft on either side of the cleavage site, as well as coordination between binding affinity for the catalytic domain and CBM3c. The same combinations of amino acid variants in the whole enzyme, Cel9A-90, did not increase BC or SWC activity but did have higher filter paper (FP) activity at 12% digestion.Cellulases catalyze the breakdown of cellulose into simple sugars that can be fermented to ethanol. The large amount of natural cellulose available is an exciting potential source of fuels and chemicals. However, the detailed molecular mechanisms of crystalline cellulose degradation by glycoside hydrolases are still not well understood and their low efficiency is a major barrier to cellulosic ethanol production.Thermobifida fusca is a filamentous soil bacterium that grows at 50°C in defined medium and can utilize cellulose as its sole carbon source. It is a major degrader of plant cell walls in heated organic materials such as compost piles and rotting hay and produces a set of enzymes that includes six different cellulases, three xylanases, a xyloglucanase, and two CBM33 binding proteins (12). Among them are three endocellulases, Cel9B, Cel6A, and Cel5A (7, 8), two exocellulases, Cel48A and Cel6B (6, 19), and a processive endocellulase, Cel9A (5, 7).T. fusca Cel9A-90 (Uniprot {"type":"entrez-protein","attrs":{"text":"P26221","term_id":"2506384","term_text":"P26221"}}P26221 and {"type":"entrez-protein","attrs":{"text":"YP_290232","term_id":"72162575","term_text":"YP_290232"}}YP_290232) is a multidomain enzyme consisting of a family 9 catalytic domain (CD) rigidly attached by a short linker to a family 3c cellulose binding module (CBM3c), followed by a fibronectin III-like domain and a family 2 CBM (CBM2). Cel9A-68 consists of the family 9 CD and CBM3c. The crystal structure of this species (Fig. ​(Fig.1)1) was determined by X-ray crystallography at 1.9 Å resolution (Protein Data Bank [PDB] code 4tf4) (15). Previous work has shown that E424 is the catalytic acid and D58 is the catalytic base (11, 20). H125 and Y206 were shown to play an important role in activity by forming a hydrogen bonding network with D58, an important supporting residue, D55, and Glc(−1)O1. Several enzymes with amino acid changes in subsites Glc(−1) to Glc(−4) had less than 20% activity on bacterial cellulose (BC) and markedly reduced processivity. It was proposed that these modifications disturb the coordination between product release and the subsequent binding of a cellulose chain into subsites Glc(−1) to Glc(−4) (11). Another variant enzyme with a deletion of a group of amino acids forming a block at the end of the catalytic cleft, Cel9A-68 Δ(T245-L251)R252K (DEL), showed slightly improved filter paper (FP) activity and binding to BC (20).Open in a separate windowFIG. 1.Crystal structure of Cel9A-68 (PDB code 4tf4) showing the locations of the variant residues, catalytic acid E424, catalytic base D58, hydrogen bonding network residues D55, H125, and Y206, and six glucose residues, Glc(−4) to Glc(+2). Part of the linker is visible in dark blue.The CBM3c domain is critical for hydrolysis and processivity. Cel9A-51, an enzyme with the family 9 CD and the linker but without CBM3c, had low activity on carboxymethyl cellulose (CMC), BC, and swollen cellulose (SWC) and showed no processivity (4). The role of CBM3c was investigated by mutagenesis, and one modified enzyme, R557A/E559A, had impaired activity on all of these substrates but normal binding and processivity (11). Variants with changes at five other CBM3c residues were found to slightly lower the activity of the modified enzymes, while Cel9A-68 enzymes containing either F476A, D513A, or I514H were found to have slightly increased binding and processivity (11) (see Table ​Table1).1). In the present work, CBM3c has been investigated more extensively to identify residues involved in substrate binding and processivity, understand the role of CBM3c more clearly, and study the coordination between the CD and CBM3c. An additional goal was to combine amino acid variants showing increased crystalline cellulose activity to see if this further increased activity. Finally, we have investigated whether the changes that improved the activity of Cel9A-68 also enhanced the activity of intact Cel9A-90.

TABLE 1.

Activities of Cel9A-68 CBM3c variant enzymes and CD variant enzymes used to create the double variants

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

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

A Two-Pathway Analysis of Meiotic Crossing Over and Gene Conversion in Saccharomyces cerevisiae

Several apparently paradoxical observations regarding meiotic crossing over and gene conversion are readily resolved in a framework that recognizes the existence of two recombination pathways that differ in mismatch repair, structures of intermediates, crossover interference, and the generation of noncrossovers. One manifestation of these differences is that simultaneous gene conversion on both sides of a recombination-initiating DNA double-strand break (“two-sidedness”) characterizes only one of the two pathways and is promoted by mismatch repair. Data from previous work are analyzed quantitatively within this framework, and a molecular model for meiotic double-strand break repair based on the concept of sliding D-loops is offered as an efficient scheme for visualizing the salient results from studies of crossing over and gene conversion, the molecular structures of recombination intermediates, and the biochemical competencies of the proteins involved.EUKARYOTES transit from the diplophase to the haplophase via meiosis, which is associated with a number of interrelated processes, including crossing over and gene conversion. These processes involve meiosis-specific, programmed DNA double-strand breaks (DSBs) and their repair (DSBr). DSBr, in turn, is associated with mismatched base pairs and their rectification, referred to as “mismatch repair” or MMR (Bishop et al. 1987). Current efforts to accommodate both the genetic and molecular phenomena associated with meiotic DSBr in yeast (Saccharomyces cerevisiae) have been thoroughly reviewed (e.g., Hollingsworth and Brill 2004; Hoffmann and Borts 2004; Surtees et al. 2004; Hunter 2007; Berchowitz and Copenhaver 2010), but none of the reviews commits to an overall picture with quantitative predictions. This work aims to remedy that lack. Specifically, we have made use of salient published studies to develop, step-by-step, a comprehensive model of meiotic DSBr and MMR. The main features of this model are summarized in FeaturesPairing pathwayDisjunction pathwayProductsCrossovers and noncrossoversCrossovers onlyCrossover InterferenceNo positive interferencePositive interferenceMsh4–Msh5 dependenceNoneTotalBimolecular intermediateLong with junctions not fully ligatedShort with fully ligated Holliday junctionsInvasion heteroduplexPartly ephemeralEphemeralMMR at invasion and annealingDependent on Msh2 and Mlh1NoneMMR near the DSB siteDirected by 3′ invading and annealing endsMlh1 dependent; directed by junction resolutionRole of Msh2 in MMRRecognizes mismatches and attracts Mlh1NoneRole of Msh4–Msh5 in MMRNoneAttracts Mlh1Open in a separate window