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L^{^a}	S^{^b}	Locus	A2-397	A1-217	A1-128	A1-199	A2-422	A2-516	A2-404	A2-435	A2-473	A2-457
519	0	A2-397
667	9	A1-217	0.006
569	8	A1-128	0.006	0.93
618	12	A1-199	0.0007	0.41	0.48
344	9	A2-422	0.0003	0.17	0.21	0.51
470	14	A2-516	0.00003	0.06	0.086	0.28	0.76
508	17	A2-404	0	0.025	0.038	0.15	0.55	0.75
506	18	A2-435	0	0.015	0.024	0.104	0.45	0.62	0.86
457	21	A2-473	0	0.001	0.003	0.0163	0.15	0.21	0.34	0.43
303	16	A2-457	0	0.0009	0.0017	0.0097	0.09	0.13	0.203	0.26	0.69
503	39	A2-575	0	0	0	0.00001	0.002	0.002	0.003	0.006	0.055	0.199

3.

One- and Two-Locus Population Models With Differential Viability Between Sexes: Parallels Between Haploid Parental Selection and Genomic Imprinting

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Alexey Yanchukov 《Genetics》2009,182(4):1117-1127

A model of genomic imprinting with complete inactivation of the imprinted allele is shown to be formally equivalent to the haploid model of parental selection. When single-locus dynamics are considered, an internal equilibrium is possible only if selection acts in the opposite directions in males and females. I study a two-locus version of the latter model, in which maternal and paternal effects are attributed to the single alleles at two different loci. A necessary condition for the allele frequency equilibria to remain on the linkage equilibrium surface is the multiplicative interaction between maternal and paternal fitness parameters. In this case the equilibrium dynamics are independent at both loci and results from the single-locus model apply. When fitness parameters are additive, analytic treatment was not possible but numerical simulations revealed that stable polymorphism characterized by association between loci is possible only in several special cases in which maternal and paternal fitness contributions are precisely balanced. As in the single-locus case, antagonistic selection in males and females is a necessary condition for the maintenance of polymorphism. I also show that the above two-locus results of the parental selection model are very sensitive to the inclusion of weak directional selection on the individual''s own genotypes.PARENTAL genetic effects refer to the influence of the mother''s and father''s genotypes on the phenotypes of their offspring, not attributable just to the transfer of genes. Examples have been documented across a wide range of areas of organism biology; see, for example, Wade (1998) and and22 in Rasanen and Kruuk (2007). Parental selection is a more formal concept used in theoretical modeling and concerns situations where the fitness of the offspring depends, besides other factors, on the genotypes of its parent(s) (generalizing from Kirkpatrick and Lande 1989).

TABLE 1

Frequencies of genotypes and fitness parameterizations in model 1

	Gametes/haploids	Frequency before selection	Fitness
Zygote	Gametes/haploids	Frequency before selection			Male	Female
(A)A	A	p_fp_m	1 − α	1 − δ
(A)a	1/2 A 1/2 a	p_f(1 − p_m)	1	1
(a)A	1/2 a 1/2 A	(1 − p_f)p_m	1 − α	1 − δ
(a)a	A	(1 − p_f)(1 − p_m)	1	1

Open in a separate windowParentheses in the first column indicate maternal genotype (parental selection model) or inactivation of the maternally derived allele (imprinting model). Whether selection occurs at the diploid (first column) or subsequent haploid (second column) stage does not change the resulting allele frequencies.

TABLE 2

Offspring genotypic proportions from different mating types, sorted among four phenotypic groups/combinations of maternal and paternal effects: model 2

		Offspring genotypes/phenotypes
Parental genotypes		Paternal (φ = 1)				Joint (φ = 4)
Male	Female	AB	Ab	aB	Ab	AB	Ab	aB	ab
AB	AB					1
	Ab
	aB
	ab	(1−r)/2	r/2	r/2	(1−r)/2
Ab	AB
	Ab		1
	aB					r/2	(1−r)/2	(1−r)/2	r/2
	ab
		Offspring genotypes/phenotypes
Parental genotypes		Maternal (φ = 2)				None (φ = 3)
Male	Female	AB	Ab	aB	Ab	AB	Ab	aB	ab
aB	AB
	Ab					r/2	(1 − r)/2	(1 − r)/2	r/2
	aB			1
	ab
ab	AB	(1 − r)/2			(1 − r)/2
	Ab
	aB
	ab								1

Open in a separate windowAnother well-known parent-of-origin phenomenon is genomic imprinting. Here, the level of expression of one of the alleles depends on which parent it is inherited from. Often it is difficult to tell apart the phenotypic patterns due to parental effects and genomic imprinting, and thus a problem arises in the process of identifying the candidate genes for such effects (). Analytic methods (; ; ) have been developed to quantify subtle differences between the two. In this article, I point out that a simple mathematical model, first suggested for genomic imprinting at a diploid locus, can be interpreted, without any formal changes, to describe parental selection on haploids.While there has been much progress in understanding the evolution of genomic imprinting (), including advances in modeling (, ), the population genetics theory of parental effects received less attention. Existing major-locus effect models of parental selection are single-locus, two-allele, and mostly concern uniparental (maternal) selection (Wright 1969; ; ; ), with only one specific case where the fitness effects of both parents interact studied by . No attempt to extend this theory into multilocus systems has yet been made. Considering a two-locus model with both parents playing a role in selection on the offspring is called for by the observation that many maternal and paternal effects aim at the different traits or different life stages of their progeny. Among birds, for example, body condition soon after hatching is largely determined by the mother, while paternally transmitted sexual display traits develop much later in life (Price 1998). Such effects are therefore unlikely to be regulated within a single locus. Sometimes the effects are on the same trait, but still attributed to different loci: expression of gene A^vy that causes the “agouti” phenotype (yellow fur coat and obesity) in mice is enhanced by maternal epigenetic modification (), while paternal mutations at the other locus, MommeD4, contribute to a reverse phenotypic pattern in the offspring (). The epigenetic state of the murine Axin^Fu allele is both maternally and paternally inherited ().Focusing selection on haploids reduces the number of genotypes that need to be taken into account, while preserving the main properties of the multilocus system. Genes with haploid expression and a potential of parental effects can be found in two major taxonomic kingdoms. A notable candidate is Spam1 in mice, which is expressed during spermogenesis and encodes a factor that enables sperm to penetrate the egg cumulus (). This gene remains a target for effectively haploid selection, because its product is not shared via cytoplasm bridges between developing spermatides. Mutations at Spam1 alter performance of the male gametes that carry it and might indirectly, perhaps by altering the timing of fertilization, affect the fitness of the zygote. The highest estimated number of mouse genes expressed in the male gametes is currently 2375 (Joseph and Kirkpatrick 2004), and one might expect some of them to have similar paternal effects. Plants go through a profound haploid stage in their life cycles, and genes involved at this stage have an inevitable effect on the fitness of the future generations. In angiosperms, seed development is known to be controlled by both maternal (; ) and paternal () effect genes, expressed, respectively, in female and male gametophytes.Under haploid selection, there can be no overdominance, and thus polymorphism is much more difficult to maintain than in diploid selection models (summarized in ). Nevertheless, differential or antagonistic selection between sexes can lead to a new class of stable internal equilibria in the diploid systems (Owen 1953; ; ; ; ), and I make use of this property in the haploid models developed below. In the experiment by Chippindale and colleagues (), ∼75% of the total fitness variation in the adult stage of Drosophila melanogaster was negatively correlated between males and females, which suggests that a substantial portion of the fruit fly expressed genome is under sexually antagonistic selection. I assume that the effect of either parent on the fitness of the individual depends on the sex of the latter, which in respect to modeling is equivalent to the assumption of differential viability between the sexes in the progeny of the same parent(s). Biological systems that satisfy the latter assumptions can be found among colonial green algae: many members of the order Volvocales are haploid except for the short zygotic stage, and during sexual reproduction, they are also dioecious and anisogametic. I return to this example in the discussion. The possibility that genes expressed in animal gametes may be under antagonistic selection between sexes has been discussed (). For example, a (hypothetical) mutation increasing the ATP production in mitochondria would be beneficial in sperm, because of the increased mobility of the latter, but neutral or detrimental in the egg, due to a higher level of oxidative damage to DNA ().My main purpose was to derive conditions for existence and stability of the internal equilibria of the model(s). I begin with a simple one-locus case, which can be analyzed explicitly, and show how these one-locus results can be extended to the case of two recombining loci with multiplicative fitness. Then, I assume an additive relation between the maternal and paternal effect parameters and study the special cases where parental effects are symmetric. 相似文献

4.

Genetic Testing of the Hypothesis That Hybrid Male Lethality Results From a Failure in Dosage Compensation

Daniel A. Barbash 《Genetics》2010,184(1):313-316

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5.

Retrograde Intraflagellar Transport Mutants Identify Complex A Proteins With Multiple Genetic Interactions in Chlamydomonas reinhardtii

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Carlo Iomini Linya Li Jessica M. Esparza Susan K. Dutcher 《Genetics》2009,183(3):885-896

The intraflagellar transport machinery is required for the assembly of cilia. It has been investigated by biochemical, genetic, and computational methods that have identified at least 21 proteins that assemble into two subcomplexes. It has been hypothesized that complex A is required for retrograde transport. Temperature-sensitive mutations in FLA15 and FLA17 show defects in retrograde intraflagellar transport (IFT) in Chlamydomonas. We show that IFT144 and IFT139, two complex A proteins, are encoded by FLA15 and FLA17, respectively. The fla15 allele is a missense mutation in a conserved cysteine and the fla17 allele is an in-frame deletion of three exons. The flagellar assembly defect of each mutant is rescued by the respective transgenes. In fla15 and fla17 mutants, bulges form in the distal one-third of the flagella at the permissive temperature and this phenotype is also rescued by the transgenes. These bulges contain the complex B component IFT74/72, but not α-tubulin or p28, a component of an inner dynein arm, which suggests specificity with respect to the proteins that accumulate in these bulges. IFT144 and IFT139 are likely to interact with each other and other proteins on the basis of three distinct genetic tests: (1) Double mutants display synthetic flagellar assembly defects at the permissive temperature, (2) heterozygous diploid strains exhibit second-site noncomplemention, and (3) transgenes confer two-copy suppression. Since these tests show different levels of phenotypic sensitivity, we propose they illustrate different gradations of gene interaction between complex A proteins themselves and with a complex B protein (IFT172).CILIA and flagella are microtubule-based organelles that are found on most mammalian cells. They provide motility to cells and participate in many sensory processes. Defects in or loss of cilia/flagella cause a variety of human diseases that include polycystic kidney disease, retinal degeneration, infertility, obesity, respiratory defects, left–right axis determination, and polydactyly (). Mouse mutants demonstrate that cilia are essential for viability, neural tube closure, and bone development (; ). Cilia and flagella are also present in protists, algae, moss, and some fungi.The assembly and maintenance of cilia and flagella require intraflagellar transport (IFT) (). IFT involves the movement of 100- to 200-nm-long protein particles from the basal body located in the cell body to the tip of the flagella using the heterotrimeric kinesin-2 (anterograde movement) () and movement back to the cell body (retrograde movement) using the cytoplasmic dynein complex (; ). IFT particles change their direction of movement as well as their size, speed, and frequency at the ends of the flagella as they switch from anterograde to retrograde movement (). Biochemical isolation of IFT particles reveals that they are composed of at least 16 proteins and that these particles can be dissociated into two complexes in vitro by changing the salt concentration (; ). Recent genetic and bioinformatics analysis adds at least 7 more proteins to the IFT particle () (Eggenschwiler and Anderson 2007).

TABLE 1

Proteins and gene names for the intraflagellar transport particles in Chlamydomonas, C. elegans, and mouse

Protein	Motif	Chlamydomonas gene	C. elegans gene	Mouse gene	References to worm and mouse genes
Complex A
IFT144	WD	FLA15
IFT140	WD	—	che-11		Qin et al. (2001)
IFT139	TRP	FLA17	dyf-2	THM1	Efimenko et al. (2006); Tran et al. (2008)
IFT122	WD	—	IFTA-1		Blacque et al. (2006)
IFT121	WD	—	daf-10		Bell et al. (2006)
IFT43		—
Complex B
IFT172	WD	FLA11	osm-1	Wimple	Huangfu et al. (2003); Pedersen et al. (2005); Bell et al. (2006)
IFT88	TRP	IFT88	osm-5	Tg737/Polaris	Pazour et al. (2000); Qin et al. (2001)
IFT81	Coil	—	ift-81	CDV1	Kobayashi et al. (2007)
IFT80	WD	—	che-2	Wdr56	Fujiwara et al. (1999)
IFT74/72	Coil	—	ift-74	Cmg1	Kobayashi et al. (2007)
IFT57/55	Coil	—	che-13	Hippi	Haycraft et al. (2003)
IFT54	Microtubule binding domain MIP-T3	—	dyf-11	Traf3IP1	Kunitomo and Iino (2008); Li et al. (2008); Omori et al. (2008); Follit et al. (2009)
IFT52	ABC type	BLD1	osm-6	Ngd2	Brazelton et al. (2001); Bell et al. (2006)
IFT46		IFT46	dyf-6		Bell et al. (2006); Hou et al. (2007)
IFT27	G protein	—	Not present	Rabl4
IFT25	Hsp20	—	Not present	HSP16.1	Follit et al. (2009)
IFT22	G protein	—	IFTA-2	Rabl5	Schafer et al. (2006)
IFT20	Coil	—			Follit et al. (2006)
FAP22	Cluamp related protein	—	dyf-3	Cluamp1	Murayama et al. (2005); Follit et al. (2009)
DYF13		—	dyf-13	Ttc26	Blacque et al. (2005)

Open in a separate window—, no mutant found to date in Chlamydomonas.A collection of temperature-sensitive mutant strains that fail to assemble flagella at the restrictive temperature of 32° was isolated in Chlamydomonas (; ; ; ). Analysis of the flagella at 21° permits the measurement of the velocity and frequency of IFT particles in the mutant strains. This analysis suggested that assembly has four phases: recruitment to the basal body, anterograde movement (phases I and II), retrograde movement, and return to the cytoplasm (phases III and IV) (). Different mutants were classified as defective in these four phases. However, because different alleles of FLA8 were classified as defective in different phases (; ), we combined mutants with IFT defects into just two classes. The first group (phases I and II) includes mutant strains that show decreased anterograde velocities, a decreased ratio of anterograde to retrograde particles, and an accumulation of complex A proteins at the basal body. This group includes mutations in the FLA8 and FLA10 genes, which encode the two motor subunits of kinesin-2 (; ), as well as mutations in three unknown genes (FLA18, FLA27, and FLA28). The second group includes mutant strains that show the reciprocal phenotype (phases III and IV); these phenotypes include decreased retrograde velocities, an increased ratio of anterograde to retrograde particles, and an accumulation of complex B proteins in the flagella. With the exception of the FLA11 gene, which encodes IFT172, a component of complex B (), the gene products in this class are unknown (FLA2, FLA15, FLA16, FLA17, and FLA24). One might predict that mutations in this group would map to genes that encode complex A or retrograde motor subunits. Interestingly, IFT particles isolated from fla11, fla15, fla16, and fla17-1 flagella show depletion of complex A polypeptides (; ). The inclusion of IFT172 in this class is explained by the observations that IFT172 plays a role in remodeling the IFT particles at the flagellar tip to transition from anterograde to retrograde movement (). The remaining mutant strains do not show obvious defects in velocities, ratios, or accumulation at 21° and may reflect a less severe phenotype at the permissive temperature or a non-IFT role for these genes.Direct interactions occur between components of complex B. IFT81 and IFT74/72 interact to form a scaffold required for IFT complex B assembly (). IFT57 and IFT20 also interact with each other and kinesin-2 (). While physical interactions are being used to define IFT particle architecture, genetic interactions among loci encoding IFT components should be instructive regarding their function as well. To probe retrograde movement and its function, we have identified the gene products encoded by two retrograde defective mutant strains. They are FLA15 and FLA17 and encode IFT144 and IFT139, respectively. The genetic interactions of these loci provide interesting clues about the assembly of the IFT particles and possible physical interactions in the IFT particles. 相似文献

6.

Adaptive Divergence in Experimental Populations of Pseudomonas fluorescens. IV. Genetic Constraints Guide Evolutionary Trajectories in a Parallel Adaptive Radiation

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Michael J. McDonald Stefanie M. Gehrig Peter L. Meintjes Xue-Xian Zhang Paul B. Rainey 《Genetics》2009,183(3):1041-1053

The capacity for phenotypic evolution is dependent upon complex webs of functional interactions that connect genotype and phenotype. Wrinkly spreader (WS) genotypes arise repeatedly during the course of a model Pseudomonas adaptive radiation. Previous work showed that the evolution of WS variation was explained in part by spontaneous mutations in wspF, a component of the Wsp-signaling module, but also drew attention to the existence of unknown mutational causes. Here, we identify two new mutational pathways (Aws and Mws) that allow realization of the WS phenotype: in common with the Wsp module these pathways contain a di-guanylate cyclase-encoding gene subject to negative regulation. Together, mutations in the Wsp, Aws, and Mws regulatory modules account for the spectrum of WS phenotype-generating mutations found among a collection of 26 spontaneously arising WS genotypes obtained from independent adaptive radiations. Despite a large number of potential mutational pathways, the repeated discovery of mutations in a small number of loci (parallel evolution) prompted the construction of an ancestral genotype devoid of known (Wsp, Aws, and Mws) regulatory modules to see whether the types derived from this genotype could converge upon the WS phenotype via a novel route. Such types—with equivalent fitness effects—did emerge, although they took significantly longer to do so. Together our data provide an explanation for why WS evolution follows a limited number of mutational pathways and show how genetic architecture can bias the molecular variation presented to selection.UNDERSTANDING—and importantly, predicting—phenotypic evolution requires knowledge of the factors that affect the translation of mutation into phenotypic variation—the raw material of adaptive evolution. While much is known about mutation rate (e.g., ; ), knowledge of the processes affecting the translation of DNA sequence variation into phenotypic variation is minimal.Advances in knowledge on at least two fronts suggest that progress in understanding the rules governing the generation of phenotypic variation is possible (). The first stems from increased awareness of the genetic architecture underlying specific adaptive phenotypes and recognition of the fact that the capacity for evolutionary change is likely to be constrained by this architecture (Schlichting and Murren 2004; Hansen 2006). The second is the growing number of reports of parallel evolution (e.g., Pigeon et al. 1997; ; ; ; ; ; ; ; ; ; ; ; )—that is, the independent evolution of similar or identical features in two or more lineages—which suggests the possibility that evolution may follow a limited number of pathways (Schluter 1996). Indeed, giving substance to this idea are studies that show that mutations underlying parallel phenotypic evolution are nonrandomly distributed and typically clustered in homologous genes ().While the nonrandom distribution of mutations during parallel genetic evolution may reflect constraints due to genetic architecture, some have argued that the primary cause is strong selection (e.g., ; ). A means of disentangling the roles of population processes (selection) from genetic architecture is necessary for progress (Maynard Smith et al. 1985; ); also necessary is insight into precisely how genetic architecture might bias the production of mutations presented to selection.Despite their relative simplicity, microbial populations offer opportunities to advance knowledge. The wrinkly spreader (WS) morphotype is one of many different niche specialist genotypes that emerge when experimental populations of Pseudomonas fluorescens are propagated in spatially structured microcosms (). Previous studies defined, via gene inactivation, the essential phenotypic and genetic traits that define a single WS genotype known as LSWS (, ) (Figure 1). LSWS differs from the ancestral SM genotype by a single nonsynonymous nucleotide change in wspF. Functionally (see Figure 2), WspF is a methyl esterase and negative regulator of the WspR di-guanylate cyclase (DGC) () that is responsible for the biosynthesis of c-di-GMP (), the allosteric activator of cellulose synthesis enzymes (). The net effect of the wspF mutation is to promote physiological changes that lead to the formation of a microbial mat at the air–liquid interface of static broth microcosms ().Open in a separate window Figure 1.—Outline of experimental strategy for elucidation of WS-generating mutations and their subsequent identity and distribution among a collection of independently evolved, spontaneously arising WS genotypes. The strategy involves, first, the genetic analysis of a specific WS genotype (e.g., LSWS) to identify the causal mutation, and second, a survey of DNA sequence variation at specific loci known to harbor causal mutations among a collection of spontaneously arising WS genotypes. For example, suppressor analysis of LSWS using a transposon to inactivate genes necessary for expression of the wrinkly morphology delivered a large number of candidate genes (top left) (). Genetic and functional analysis of these candidate genes (e.g., ) led eventually to the identity of the spontaneous mutation (in wspF) responsible for the evolution of LSWS from the ancestral SM genotype (). Subsequent analysis of the wspF sequence among 26 independent WS genotypes (bottom) showed that 50% harbored spontaneous mutations (of different kinds; see Open in a separate window Figure 2.—Network diagram of DGC-encoding pathways underpinning the evolution of the WS phenotype and their regulation. Overproduction of c-di-GMP results in overproduction of cellulose and other adhesive factors that determine the WS phenotype. The ancestral SBW25 genome contains 39 putative DGCs, each in principle capable of synthesizing the production of c-di-GMP, and yet WS genotypes arise most commonly as a consequence of mutations in just three DGC-containing pathways: Wsp, Aws, and Mws. In each instance, the causal mutations are most commonly in the negative regulatory component: wspF, awsX, and the phosphodiesterase domain of mwsR (see text).To determine whether spontaneous mutations in wspF are a common cause of the WS phenotype, the nucleotide sequence of this gene was obtained from a collection of 26 spontaneously arising WS genotypes (WS_A-Z) taken from 26 independent adaptive radiations, each founded by the same ancestral SM genotype (Figure 1): 13 contained mutations in wspF (Bantinaki et al. 2007). The existence of additional mutational pathways to WS provided the initial motivation for this study.

TABLE 1

Mutational causes of WS

WS genotype	Gene	Nucleotide change	Amino acid change	Source/reference
LSWS	wspF	A901C	S301R	Bantinaki et al. (2007)
AWS	awsX	Δ100-138	ΔPDPADLADQRAQA	This study
MWS	mwsR	G3247A	E1083K	This study
WS_A	wspF	T14G	I5S	Bantinaki et al. (2007)
WS_B	wspF	Δ620-674	P206Δ (8)^{^a}	Bantinaki et al. (2007)
WS_C	wspF	G823T	G275C	Bantinaki et al. (2007)
WS_D	wspE	A1916G	D638G	This study
WS_E	wspF	G658T	V220L	Bantinaki et al. (2007)
WS_F	wspF	C821T	T274I	Bantinaki et al. (2007)
WS_G	wspF	C556T	H186Y	Bantinaki et al. (2007)
WS_H	wspE	A2202C	K734N	This study
WS_I	wspE	G1915T	D638Y	This study
WS_J	wspF	Δ865-868	R288Δ (3)^{^a}	Bantinaki et al. (2007)
WS_K	awsO	G125T	G41V	This study
WS_L	wspF	G482A	G161D	Bantinaki et al. (2007)
WS_M	awsR	C164T	S54F	This study
WS_N	wspF	A901C	S301R	Bantinaki et al. (2007)
WS_O	wspF	Δ235-249	V79Δ (6)^{^a}	Bantinaki et al. (2007)
WS_P	awsR	222insGCCACCGAA	74insATE	This study
WS_Q	mwsR	3270insGACGTG	1089insDV	This study
WS_R	mwsR	T2183C	V272A	This study
WS_S	awsX	C472T	Q158STOP	This study
WS_T	awsX	Δ229-261	ΔYTDDLIKGTTQ	This study
WS_U	wspF	Δ823-824	T274Δ (13)^{^a}	Bantinaki et al. (2007)
WS_V	awsX	T74G	L24R	This study
WS_W	wspF	Δ149	L49Δ (1)^{^a}	Bantinaki et al. (2007)
WS_X^{^b}	?	?	?	This study
WS_Y	wspF	Δ166-180	Δ(L51-I55)	Bantinaki et al. (2007)
WS_Z	mwsR	G3055A	A1018T	This study

Open in a separate window^aP206Δ(8) indicates a frameshift; the number of new residues before a stop codon is reached is in parentheses.^bSuppressor analysis implicates the wsp locus (17 transposon insertions were found in this locus). However, repeated sequencing failed to identify a mutation.Here we define and characterize two new mutational routes (Aws and Mws) that together with the Wsp pathway account for the evolution of 26 spontaneously arising WS genotypes. Each pathway offers approximately equal opportunity for WS evolution; nonetheless, additional, less readily realized genetic routes producing WS genotypes with equivalent fitness effects exist. Together our data show that regulatory pathways with specific functionalities and interactions bias the molecular variation presented to selection. 相似文献

7.

Experimentally Increased Codon Bias in the Drosophila Adh Gene Leads to an Increase in Larval,But Not Adult,Alcohol Dehydrogenase Activity

Winfried Hense Nathan Anderson Stephan Hutter Wolfgang Stephan John Parsch David B. Carlini 《Genetics》2010,184(2):547-555

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8.

Construction and Characterization of Three Lactate Dehydrogenase-Negative Enterococcus faecalis V583 Mutants

Maria J?nsson Zhian Saleihan Ingolf F. Nes Helge Holo 《Applied and environmental microbiology》2009,75(14):4901-4903

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9.

Characterization of a Thermostable Short-Chain Alcohol Dehydrogenase from the Hyperthermophilic Archaeon Thermococcus sibiricus

Tatiana N. Stekhanova Andrey V. Mardanov Ekaterina Y. Bezsudnova Vadim M. Gumerov Nikolai V. Ravin Konstantin G. Skryabin Vladimir O. Popov 《Applied and environmental microbiology》2010,76(12):4096-4098

Short-chain alcohol dehydrogenase, encoded by the gene Tsib_0319 from the hyperthermophilic archaeon Thermococcus sibiricus, was expressed in Escherichia coli, purified and characterized as an NADPH-dependent enantioselective oxidoreductase with broad substrate specificity. The enzyme exhibits extremely high thermophilicity, thermostability, and tolerance to organic solvents and salts.Alcohol dehydrogenases (ADHs; EC 1.1.1.1.) catalyze the interconversion of alcohols to their corresponding aldehydes or ketones by using different redox-mediating cofactors. NAD(P)-dependent ADHs, due to their broad substrate specificity and enantioselectivity, have attracted particular attention as catalysts in industrial processes (5). However, mesophilic ADHs are unstable at high temperatures, sensitive to organic solvents, and often lose activity during immobilization. In this relation, there is a considerable interest in ADHs from extremophilic microorganisms; among them, Archaea are of great interest. The representatives of all groups of NAD(P)-dependent ADHs have been detected in genomes of Archaea (, 12); however, only a few enzymes have been characterized, and the great majority of them belong to medium-chain (, , , , ) or long-chain iron-activated ADHs (, , ). Up to now, a single short-chain archaeal ADH from Pyrococcus furiosus (, ) and only one archaeal aldo-keto reductase also from P. furiosus () have been characterized.Thermococcus sibiricus is a hyperthermophilic anaerobic archaeon isolated from a high-temperature oil reservoir capable of growth on complex organic substrates (). The complete genome sequence of T. sibiricus has been recently determined and annotated (). Several ADHs are encoded by the T. sibiricus genome, including three short-chain ADHs (Tsib_0319, Tsib_0703, and Tsib_1998) (). In this report, we describe the cloning and expression of the Tsib_0319 gene from T. sibiricus and the purification and the biochemical characterization of its product, the thermostable short-chain ADH (TsAdh319).The Tsib_0319 gene encodes a protein with a size of 234 amino acids and the calculated molecular mass of 26.2 kDa. TsAdh319 has an 85% degree of sequence identity with short-chain ADH from P. furiosus (AdhA; PF_0074) (). Besides AdhA, close homologs of TsAdh319 were found among different bacterial ADHs, but not archaeal ADHs. The gene flanked by the XhoI and BamHI sites was PCR amplified using two primers (sense primer, 5′-GTTCTCGAGATGAAGGTTGCTGTGATAACAGGG-3′, and antisense primer, 5′-GCTGGATCCTCAGTATTCTGGTCTCTGGTAGACGG-3′) and cloned into the pET-15b vector. TsAdh319 was overexpressed, with an N-terminal His₆ tag in Escherichia coli Rosetta-gami (DE3) and purified to homogeneity by metallochelating chromatography (Hi-Trap chelating HP column; GE Healthcare) followed by gel filtration on Superdex 200 10/300 GL column (GE Healthcare) equilibrated in 50 mM Tris-HCl (pH 7.5) with 200 mM NaCl. The homogeneity and the correspondence to the calculated molecular mass of 28.7 kDa were verified by SDS-PAGE (). The molecular mass of native TsAdh319 was 56 to 60 kDa, which confirmed the dimeric structure in solution.The standard ADH activity measurement was made spectrophotometrically at the optimal pH by following either the reduction of NADP (in 50 mM Gly-NaOH buffer; pH 10.5) or the oxidation of NADPH (in 0.1 M sodium phosphate buffer; pH 7.5) at 340 nm at 60°C. The enzyme exhibited a strong preference for NADP(H) and broad substrate specificity (Table (Table1).1). The highest oxidation rates were found with pentoses d-arabinose (2.0 U mg⁻¹) and d-xylose (2.46 U mg⁻¹), and the highest reduction rates were found with dimethylglyoxal (5.9 U mg⁻¹) and pyruvaldehyde (2.2 U mg⁻¹). The enzyme did not reduce sugars which were good substrates for the oxidation reaction. The kinetic parameters of TsAdh319 determined for the preferred substrates are shown in Table Table2.2. The enantioselectivity of the enzyme was estimated by measuring the conversion rates of 2-butanol enantiomers. TsAdh319 showed an evident preference, >2-fold, for (S)-2-butanol over (RS)-2-butanol. The enzyme stereoselectivity is confirmed by the preferred oxidation of d-arabinose over l-arabinose (Table (Table1).1). The fact that TsAdh319 is metal independent was supported by the absence of a significant effect of TsAdh319 preincubation with 10 mM Me²⁺ for 30 min before measuring the activity in the presence of 1 mM Me²⁺ or EDTA (Table (Table3).3). TsAdh319 also exhibited a halophilic property, so the enzyme activity increased in the presence of NaCl and KCl and the activation was maintained even at concentration of 4 M and 3 M, respectively (Table (Table33).

TABLE 1.

Substrate specificity of TsAdh319

Substrate^a	Relative activity (%)
Oxidation reaction^b
Methanol	0
2-Methoxyethanol	0
Ethanol	36
1-Butanol	80
2-Propanol	100
(RS)-(±)-2-Butanol	86
(S)-(+)-2-Butanol	196
2-Pentanol	67
1-Phenylmethanol	180
1.3-Butanediol	91
Ethyleneglycol	0
Glycerol	16
d-Arabinose*	200
l-Arabinose*	17
d-Xylose*	246
d-Ribose*	35
d-Glucose*	146
d-Mannose*	48
d-Galactose*	0
Cellobiose*	71
Reduction reaction^c
Pyruvaldehyde	100
Dimethylglyoxal	270
Glyoxylic acid	36
Acetone	0
Cyclopentanone	0
Cyclohexanone	4
3-Methyl-2-pentanone*	13
d-Arabinose*	0
d-Xylose*	0
d-Glucose*	0
Cellobiose*	0

Open in a separate window^aSubstrates were present in 250 mM or 50 mM (*) concentrations.^bRelative rates, measured under standard conditions, were calculated by defining the activity for 2-propanol as 100%, which corresponds to 1.0 U mg⁻¹. Data are averages from triplicate experiments.^cRelative rates, measured under standard conditions, were calculated by defining the activity for pyruvaldehyde as 100%, which corresponds to 2.2 U mg⁻¹. Data are averages from triplicate experiments.

TABLE 2.

Apparent K_m and V_max values for TsAdh319

Coenzyme or substrate	Apparent K_m (mM)	V_max (U mg⁻¹)	k_cat (s⁻¹)
NADP^a	0.022 ± 0.002	0.94 ± 0.02	0.45 ± 0.01
NADPH^b	0.020 ± 0.003	3.16 ± 0.11	1.51 ± 0.05
2-Propanol	168 ± 29	1.10 ± 0.09	0.53 ± 0.04
d-Xylose	54.4 ± 7.4	1.47 ± 0.09	0.70 ± 0.04
Pyruvaldehyde	17.75 ± 3.38	4.26 ± 0.40	2.04 ± 0.19

Open in a separate window^aActivity was measured under standard conditions with 2-propanol. Data are averages from triplicate experiments.^bActivity was measured under standard conditions with pyruvaldehyde. Data are averages from triplicate experiments.

TABLE 3.

Effect of various ions and EDTA on TsAdh319^a

Compound	Concn (mM)	Relative activity (%)
None	0	100
NaCl	400	206
	600	227
	4,000	230
KCl	600	147
	2,000	200
	3,000	194
MgCl₂	10	78
CoCl₂	10	105
NiSO₄	10	100
ZnSO₄	10	79
FeSO₄	10	74
EDTA	1	100
EDTA	5	80

Open in a separate window^aThe activity was measured under standard conditions with 2-propanol; relative rates were calculated by defining the activity without salts as 100%, which corresponds to 0.9 U mg⁻¹. Data are averages from duplicate experiments.The most essential distinctions of TsAdh319 are the thermophilicity and high thermostability of the enzyme. The optimum temperature for the 2-propanol oxidation catalyzed by TsAdh319 was not achieved. The initial reaction rate of oxidation increased up to 100°C (Fig. (Fig.1).1). The Arrhenius plot is a straight line, typical of a single rate-limited thermally activated process, but there is no obvious transition point due to the temperature-dependent conformational changes of the protein molecule. The activation energy for the oxidation of 2-propanol was estimated at 84.0 ± 5.8 kJ·mol⁻¹. The thermostability of TsAdh319 was calculated from residual TsAdh319 activity after preincubation of 0.4 mg/ml enzyme solution in 50 mM Tris-HCl buffer (pH 7.5) containing 200 mM NaCl at 70, 80, 90, or 100°C. The preincubation at 70°C or 80°C for 1.5 h did not cause a decrease in the TsAdh319 activity, but provoked slight activation. The residual TsAdh319 activities began to decrease after 2 h of preincubation at 70°C or 80°C and were 10% and 15% down from the control, respectively. The determined half-life values of TsAdh319 were 2 h at 90°C and 1 h at 100°C.Open in a separate window FIG. 1.Temperature dependence of the initial rate of the 2-propanol reduction by TsAdh319. The reaction was initiated by enzyme addition to a prewarmed 2-propanol-NADP mixture. The inset shows the Arrhenius plot of the same data.Protein thermostability often correlates with such important biotechnological properties as increased solvent tolerance (2). We tested the influence of organic solvents at a high concentration (50% [vol/vol]) on TsAdh319 by using either preincubation of the enzyme at a concentration of 0.2 mg/ml with solvents for 4 h at 55°C or solvent addition into the reaction mixture to distinguish the effect of solvent on the protein stability and on the enzyme activity. TsAdh319 showed significant solvent tolerance in both cases (Table (Table4),4), and the effects of solvents could be modulated by salts, acting apparently as molecular lyoprotectants (). Furthermore, TsAdh319 maintained 57% of its activity in 25% (vol/vol) 2-propanol, which could be used as the cosubstrate in cofactor regeneration ().

TABLE 4.

Influence of various solvents on TsAdh319 activity^a

Solvent	Relative activity (%)^b	Relative activity (%)^c
Solvent	Relative activity (%)^b	Buffer without NaCl	Buffer with 600 mM NaCl
None	100	100	100
DMSO^d	98	0	40
DMFA^e	101	13	41
Methanol	98	25	9
Acetonitrile	95	0	0
Ethyl acetate	47	0*	33*
Chloroform	105	79*	81*
n-Hexane	105	60*	118*
n-Decane	36	91*	107*

Open in a separate window^aThe activity measured at the standard condition with 2-propanol as a substrate. Data are averages from triplicate experiments.^bPreincubation for 4 h at 55°C in the presence of 50% (vol/vol) of solvent prior the activity assay.^cWithout preincubation, solvent addition to the reaction mixture up to 50% (vol/vol) or using the buffer saturated by a solvent (*).^dDMSO, dimethyl sulfoxide.^eDMFA, dimethylformamide.From all the aforesaid we may suppose TsAdh319 or its improved variant to be interesting both for the investigation of structural features of protein tolerance and for biotechnological applications. 相似文献

10.

Dominant Bacteria and Biomass in the Kuytun 51 Glacier

Shu-Rong Xiang Tian-Cui Shang Yong Chen Ze-Fan Jing Tandong Yao 《Applied and environmental microbiology》2009,75(22):7287-7290

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11.

RNA-Based Investigation of Ammonia-Oxidizing Archaea in Hot Springs of Yunnan Province,China

Hongchen Jiang Qiuyuan Huang Hailiang Dong Peng Wang Fengping Wang Wenjun Li Chuanlun Zhang 《Applied and environmental microbiology》2010,76(13):4538-4541

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12.

Antimicrobial Activity of Simulated Solar Disinfection against Bacterial,Fungal, and Protozoan Pathogens and Its Enhancement by Riboflavin

Wayne Heaselgrave Simon Kilvington 《Applied and environmental microbiology》2010,76(17):6010-6012

Riboflavin significantly enhanced the efficacy of simulated solar disinfection (SODIS) at 150 watts per square meter (W m⁻²) against a variety of microorganisms, including Escherichia coli, Fusarium solani, Candida albicans, and Acanthamoeba polyphaga trophozoites (>3 to 4 log₁₀ after 2 to 6 h; P < 0.001). With A. polyphaga cysts, the kill (3.5 log₁₀ after 6 h) was obtained only in the presence of riboflavin and 250 W m⁻² irradiance.Solar disinfection (SODIS) is an established and proven technique for the generation of safer drinking water (). Water is collected into transparent plastic polyethylene terephthalate (PET) bottles and placed in direct sunlight for 6 to 8 h prior to consumption (). The application of SODIS has been shown to be a simple and cost-effective method for reducing the incidence of gastrointestinal infection in communities where potable water is not available (-). Under laboratory conditions using simulated sunlight, SODIS has been shown to inactivate pathogenic bacteria, fungi, viruses, and protozoa (, , ). Although SODIS is not fully understood, it is believed to achieve microbial killing through a combination of DNA-damaging effects of ultraviolet (UV) radiation and thermal inactivation from solar heating (21).The combination of UVA radiation and riboflavin (vitamin B₂) has recently been reported to have therapeutic application in the treatment of bacterial and fungal ocular pathogens (, ) and has also been proposed as a method for decontaminating donor blood products prior to transfusion (). In the present study, we report that the addition of riboflavin significantly enhances the disinfectant efficacy of simulated SODIS against bacterial, fungal, and protozoan pathogens.Chemicals and media were obtained from Sigma (Dorset, United Kingdom), Oxoid (Basingstoke, United Kingdom), and BD (Oxford, United Kingdom). Pseudomonas aeruginosa (ATCC 9027), Staphylococcus aureus (ATCC 6538), Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), and Fusarium solani (ATCC 36031) were obtained from ATCC (through LGC Standards, United Kingdom). Escherichia coli (JM101) was obtained in house, and the Legionella pneumophila strain used was a recent environmental isolate.B. subtilis spores were produced from culture on a previously published defined sporulation medium (). L. pneumophila was grown on buffered charcoal-yeast extract agar (). All other bacteria were cultured on tryptone soy agar, and C. albicans was cultured on Sabouraud dextrose agar as described previously (9). Fusarium solani was cultured on potato dextrose agar, and conidia were prepared as reported previously (). Acanthamoeba polyphaga (Ros) was isolated from an unpublished keratitis case at Moorfields Eye Hospital, London, United Kingdom, in 1991. Trophozoites were maintained and cysts prepared as described previously (, 18).Assays were conducted in transparent 12-well tissue culture microtiter plates with UV-transparent lids (Helena Biosciences, United Kingdom). Test organisms (1 × 10⁶/ml) were suspended in 3 ml of one-quarter-strength Ringer''s solution or natural freshwater (as pretreated water from a reservoir in United Kingdom) with or without riboflavin (250 μM). The plates were exposed to simulated sunlight at an optical output irradiance of 150 watts per square meter (W m⁻²) delivered from an HPR125 W quartz mercury arc lamp (Philips, Guildford, United Kingdom). Optical irradiances were measured using a calibrated broadband optical power meter (Melles Griot, Netherlands). Test plates were maintained at 30°C by partial submersion in a water bath.At timed intervals for bacteria and fungi, the aliquots were plated out by using a WASP spiral plater and colonies subsequently counted by using a ProtoCOL automated colony counter (Don Whitley, West Yorkshire, United Kingdom). Acanthamoeba trophozoite and cyst viabilities were determined as described previously (). Statistical analysis was performed using a one-way analysis of variance (ANOVA) of data from triplicate experiments via the InStat statistical software package (GraphPad, La Jolla, CA).The efficacies of simulated sunlight at an optical output irradiance of 150 W m⁻² alone (SODIS) and in the presence of 250 μM riboflavin (SODIS-R) against the test organisms are shown in Table Table1.1. With the exception of B. subtilis spores and A. polyphaga cysts, SODIS-R resulted in a significant increase in microbial killing compared to SODIS alone (P < 0.001). In most instances, SODIS-R achieved total inactivation by 2 h, compared to 6 h for SODIS alone (Table (Table1).1). For F. solani, C. albicans, ands A. polyphaga trophozoites, only SODIS-R achieved a complete organism kill after 4 to 6 h (P < 0.001). All control experiments in which the experiments were protected from the light source showed no reduction in organism viability over the time course (results not shown).

TABLE 1.

Efficacies of simulated SODIS for 6 h alone and with 250 μM riboflavin (SODIS-R)

Organism	Condition^a	Log₁₀ reduction in viability at indicated h of exposure^b
Organism	Condition^a	1	2	4	6
E. coli	SODIS	0.0 ± 0.0	0.2 ± 0.1	5.7 ± 0.0	5.7 ± 0.0
	SODIS-R	1.1 ± 0.0	5.7 ± 0.0	5.7 ± 0.0	5.7 ± 0.0
L. pneumophila	SODIS	0.7 ± 0.2	1.3 ± 0.3	4.8 ± 0.2	4.8 ± 0.2
	SODIS-R	4.4 ± 0.0	4.4 ± 0.0	4.4 ± 0.0	4.4 ± 0.0
P. aeruginosa	SODIS	0.7 ± 0.0	1.8 ± 0.0	4.9 ± 0.0	4.9 ± 0.0
	SODIS-R	5.0 ± 0.0	5.0 ± 0.0	5.0 ± 0.0	5.0 ± 0.0
S. aureus	SODIS	0.0 ± 0.0	0.0 ± 0.0	6.2 ± 0.0	6.2 ± 0.0
	SODIS-R	0.2 ± 0.1	6.3 ± 0.0	6.3 ± 0.0	6.3 ± 0.0
C. albicans	SODIS	0.2 ± 0.0	0.4 ± 0.1	0.5 ± 0.1	1.0 ± 0.1
	SODIS-R	0.1 ± 0.0	0.7 ± 0.1	5.3 ± 0.0	5.3 ± 0.0
F. solani conidia	SODIS	0.2 ± 0.1	0.3 ± 0.0	0.2 ± 0.0	0.7 ± 0.1
	SODIS-R	0.3 ± 0.1	0.8 ± 0.1	1.3 ± 0.1	4.4 ± 0.0
B. subtilis spores	SODIS	0.3 ± 0.0	0.2 ± 0.0	0.0 ± 0.0	0.1 ± 0.0
	SODIS-R	0.1 ± 0.1	0.2 ± 0.1	0.3 ± 0.3	0.1 ± 0.0
	SODIS (250 W m⁻²)	0.1 ± 0.0	0.1 ± 0.1	0.1 ± 0.1	0.0 ± 0.0
	SODIS-R (250 W m⁻²)	0.0 ± 0.0	0.0 ± 0.0	0.2 ± 0.0	0.4 ± 0.0
	SODIS (320 W m⁻²)	0.1 ± 0.1	0.1 ± 0.0	0.0 ± 0.1	4.3 ± 0.0
	SODIS-R (320 W m⁻²)	0.1 ± 0.0	0.1 ± 0.1	0.9 ± 0.0	4.3 ± 0.0
A. polyphaga trophozoites	SODIS	0.4 ± 0.2	0.6 ± 0.1	0.6 ± 0.2	0.4 ± 0.1
	SODIS-R	0.3 ± 0.1	1.3 ± 0.1	2.3 ± 0.4	3.1 ± 0.2
	SODIS, natural^c	0.3 ± 0.1	0.4 ± 0.1	0.5 ± 0.2	0.3 ± 0.2
	SODIS-R, natural^c	0.2 ± 0.1	1.0 ± 0.2	2.2 ± 0.3	2.9 ± 0.3
A. polyphaga cysts	SODIS	0.4 ± 0.1	0.1 ± 0.3	0.3 ± 0.1	0.4 ± 0.2
	SODIS-R	0.4 ± 0.2	0.3 ± 0.2	0.5 ± 0.1	0.8 ± 0.3
	SODIS (250 W m⁻²)	0.0 ± 0.1	0.2 ± 0.3	0.2 ± 0.1	0.1 ± 0.2
	SODIS-R (250 W m⁻²)	0.4 ± 0.2	0.3 ± 0.2	0.8 ± 0.1	3.5 ± 0.3
	SODIS (250 W m⁻²), natural^c	0.0 ± 0.3	0.2 ± 0.1	0.1 ± 0.1	0.2 ± 0.1
	SODIS-R (250 W m⁻²), natural^c	0.1 ± 0.1	0.2 ± 0.2	0.6 ± 0.1	3.4 ± 0.2

Open in a separate window^aConditions are at an intensity of 150 W m⁻² unless otherwise indicated.^bThe values reported are means ± standard errors of the means from triplicate experiments.^cAdditional experiments for this condition were performed using natural freshwater.The highly resistant A. polyphaga cysts and B. subtilis spores were unaffected by SODIS or SODIS-R at an optical irradiance of 150 W m⁻². However, a significant reduction in cyst viability was observed at 6 h when the optical irradiance was increased to 250 W m⁻² for SODIS-R only (P < 0.001; Table Table1).1). For spores, a kill was obtained only at 320 W m⁻² after 6-h exposure, and no difference between SODIS and SODIS-R was observed (Table (Table1).1). Previously, we reported a >2-log kill at 6 h for Acanthamoeba cysts by using SODIS at the higher optical irradiance of 850 W m⁻², compared to the 0.1-log₁₀ kill observed here using the lower intensity of 250 W m⁻² or the 3.5-log₁₀ kill with SODIS-R.Inactivation experiments performed with Acanthamoeba cysts and trophozoites suspended in natural freshwater gave results comparable to those obtained with Ringer''s solution (P > 0.05; Table Table1).1). However, it is acknowledged that the findings of this study are based on laboratory-grade water and freshwater and that differences in water quality through changes in turbidity, pH, and mineral composition may significantly affect the performance of SODIS (). Accordingly, further studies are indicated to evaluate the enhanced efficacy of SODIS-R by using natural waters of varying composition in the areas where SODIS is to be employed.Previous studies with SODIS under laboratory conditions have employed lamps delivering an optical irradiance of 850 W m⁻² to reflect typical natural sunlight conditions (, , , , ). Here, we used an optical irradiance of 150 to 320 W m⁻² to obtain slower organism inactivation and, hence, determine the potential enhancing effect of riboflavin on SODIS.In conclusion, this study has shown that the addition of riboflavin significantly enhances the efficacy of simulated SODIS against a range of microorganisms. The precise mechanism by which photoactivated riboflavin enhances antimicrobial activity is unknown, but studies have indicated that the process may be due, in part, to the generation of singlet oxygen, H₂O₂, superoxide, and hydroxyl free radicals (). Further studies are warranted to assess the potential benefits from riboflavin-enhanced SODIS in reducing the incidence of gastrointestinal infection in communities where potable water is not available. 相似文献

13.

Cultivation and Genomic,Nutritional, and Lipid Biomarker Characterization of Roseiflexus Strains Closely Related to Predominant In Situ Populations Inhabiting Yellowstone Hot Spring Microbial Mats

Marcel T. J. van der Meer Christian G. Klatt Jason Wood Donald A. Bryant Mary M. Bateson Laurens Lammerts Stefan Schouten Jaap S. Sinninghe Damsté Michael T. Madigan David M. Ward 《Journal of bacteriology》2010,192(12):3033-3042

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14.

Cross-Subtype Neutralization Sensitivity despite Monoclonal Antibody Resistance among Early Subtype A,C, and D Envelope Variants of Human Immunodeficiency Virus Type 1

Catherine A. Blish Zahra Jalalian-Lechak Stephanie Rainwater Minh-An Nguyen Ozge C. Dogan Julie Overbaugh 《Journal of virology》2009,83(15):7783-7788

The human immunodeficiency virus type 1 (HIV-1) variants that are transmitted to newly infected individuals are the primary targets of interventions, such as vaccines and microbicides, aimed at preventing new infections. Newly acquired subtype A, B, and C variants have been the focus of neutralization studies, although many of these viruses, particularly of subtypes A and B, represent viruses circulating more than a decade ago. In order to better represent the global diversity of transmitted HIV-1 variants, an additional 31 sexually transmitted Kenyan HIV-1 env genes, representing several recent infections with subtype A, as well as subtypes A/D, C, and D, were cloned, and their neutralization profiles were characterized. Most env variants were resistant to neutralization by the monoclonal antibodies (MAbs) b12, 4E10, 2F5, and 2G12, suggesting that targeting the epitopes of these MAbs may not be effective against variants that are spreading in areas of endemicity. However, significant cross-subtype neutralization by plasma was observed, indicating that there may be other epitopes, not yet defined by the limited available MAbs, which could be recognized more broadly.Most effective viral vaccines are thought to provide protection primarily by stimulating neutralizing antibodies (NAbs) to clear cell-free virus (, ). Because protection by NAbs requires recognition of common viral epitopes, the extreme genetic diversity of human immunodeficiency virus type 1 (HIV-1) presents a particular challenge to NAb-based vaccine approaches. Therefore, a critical starting point for studies of immune-mediated protection against HIV-1 is a collection of newly transmitted HIV-1 variants, particularly from areas of endemicity, such as sub-Saharan Africa, in order to determine whether vaccines are appropriately targeted to common epitopes from these relevant transmitted strains.During HIV-1 transmission, a bottleneck allows only one or a few variants to be transmitted to a newly infected individual (, , , , , , ), and the sensitivity of these early transmitted strains to antibody-mediated neutralization is therefore of particular interest. Newly transmitted HIV-1 variants have demonstrated significant heterogeneity in their neutralization phenotypes both within and between subtypes (, , -, , -, , , , ). Panels of sexually transmitted HIV-1 envelope variants (based on the envelope gene, env) have been characterized, including subtype B variants from North America, Trinidad, and Europe, subtype C variants from South Africa and Zambia, and subtype A variants from Kenya collected between 1994 and 1996 (, , ). Here, we characterize an additional 31 envelope variants from 14 subjects with sexually transmitted HIV-1 who were infected in Kenya, where subtypes A, C, and D circulate, between 1993 and 2005 (, ).The env genes were cloned from samples drawn 14 to 391 (median, 65) days postinfection from individuals enrolled in a prospective cohort of high-risk women in Mombasa, Kenya (-). Demographic characteristics of the subjects are summarized in Table Table1;1; the timing of first infection was determined by both HIV-1 serology and HIV RNA testing as described previously (). All of the subjects were presumably infected by male-to-female transmission and displayed a range of plasma viral loads at the time of env gene cloning (Table (Table1).1). For most individuals, full-length env genes were cloned from uncultured peripheral blood mononuclear cell (PBMC) DNA, though for two individuals, clones were obtained from DNA following short-term coculture with donor PBMCs (Table (Table1).1). env genes were cloned by single-copy nested PCR with primers and PCR conditions as described previously (, ). We tested env genes for their ability to mediate infection by transfecting env plasmid DNA into 293T cells along with an env-deficient HIV-1 subtype A proviral plasmid, Q23Δenv, to make pseudoviral particles (). More than 80 env clones were obtained from 16 subjects; less than one-half were functional on the basis of the infectivity of pseudoviral particles in a single-round infection of TZM-bl cells (AIDS Research and Reference Reagent Program, National Institutes of Health), as observed previously for env genes cloned from proviral sequences (); a lower fraction of functional env genes have been reported from plasma (). We focused on the proviral sequences here because they presumably best represent the sequence closest to that of the transmitted strains. The 31 functional env variants are described in Table Table11.

TABLE 1.

Demographic characteristics, diversities, gp120 variable-region lengths, numbers of PNGS, and accession numbers of cloned env variants

Subject	Virus subtype	Sample date (mo/day/yr)	dpi^a	Plasma VL^b	Source^c	Individual env clone	Pairwise difference (%)^d	Variable-loop length (aa)				No. of PNGS			GenBank accession no.
Subject	Virus subtype	Sample date (mo/day/yr)	dpi^a	Plasma VL^b	Source^c	Individual env clone	Pairwise difference (%)^d	V1/V2	V3	V4	V5	gp120	gp41	gp41 ecto	GenBank accession no.
QB726	A	04/16/96	70	61,940	ucPBMC	QB726.70M.ENV.B3	0.16	63	35	36	10	22	4	4	FJ866111
						QB726.70M.ENV.C4		63	35	36	10	22	4	4	FJ866112
QF495	A	05/16/06	23	217,050	ucPBMC	QF495.23M.ENV.A1	0.12	107	35	37	11	30	4	4	FJ866113
						QF495.23M.ENV.A3		107	35	37	11	30	4	4	FJ866114
						QF495.23M.ENV.B2		113	35	37	11	31	4	4	FJ866115
						QF495.23M.ENV.D1		113	35	37	11	31	4	4	FJ866116
QG984	A	07/12/04	21	30,300	ucPBMC	QG984.21M.ENV.A3	NA	69	34	36	11	24	3	3	FJ866117
QH209	A	10/13/05	14	28,600	ucPBMC	QH209.14M.ENV.A2	NA	72	35	29	11	24	4	4	FJ866118
QH343	A	09/08/05	21	40,750,000	ucPBMC	QH343.21M.ENV.A10	0.19	77	35	32	15	26	4	4	FJ866119
						QH343.21M.ENV.B5		77	35	32	15	26	4	4	FJ866120
QH359	A	10/05/05	21	32,120	ucPBMC	QH359.21M.ENV.C1	1.4	84	35	36	10	29	4	4	FJ866121
						QH359.21M.ENV.D1		73	35	35	10	26	4	4	FJ866122
						QH359.21M.ENV.E2		72	35	40	13	28	4	4	FJ866123
QA790^e	A/D	06/10/96	204	48,100	ccPBMC	QA790.204I.ENV.A4	0.36	77	35	33	11	25	4	4	FJ866124
						QA790.204I.ENV.C1		77	35	33	11	26	4	4	FJ866125
						QA790.204I.ENV.C8		77	35	33	11	24	4	4	FJ866126
						QA790.204I.ENV.E2		77	35	33	11	25	4	4	FJ866127
QG393	A2/D	06/23/04	60	17,360	ucPBMC	QG393.60M.ENV.A1	0.7	60	34	31	10	24	5	5	FJ866128
						QG393.60M.ENV.B7		57	34	31	10	24	5	5	FJ866129
						QG393.60M.ENV.B8		57	34	31	10	24	5	5	FJ866130
QB099^e	C	02/10/95	391	27,280	ucPBMC	QB099.391M.ENV.B1	0.43	65	35	29	10	25	4	4	FJ866131
						QB099.391M.ENV.C8		65	35	29	10	25	4	4	FJ866132
QC406	C	07/08/97	70	692,320	ucPBMC	QC406.70M.ENV.F3	NA	64	35	20	11	22	5	4	FJ866133
QA013	D	10/11/95	70	1,527,700	ccPBMC	QA013.70I.ENV.H1	0.16	60	34	29	12	25	4	4	FJ866134
						QA013.70I.ENV.M12		60	34	29	12	25	4	4	FJ866135
QA465	D	08/19/93	59	37,750	ucPBMC	QA465.59M.ENV.A1	0.24	65	35	30	11	28	4	4	FJ866136
						QA465.59M.ENV.D1		65	35	30	11	27	4	4	FJ866137
QB857	D	10/16/97	110	14,640	ucPBMC	QB857.23I.ENV.B3	NA	68	34	32	11	26	5	4	FJ866138
QD435	D	04/06/99	100	17,470	ucPBMC	QD435.100M.ENV.A4	0.88	69	34	29	12	26	5	4	FJ866139
						QD435.100M.ENV.B5		67	34	29	11	24	5	4	FJ866140
						QD435.100M.ENV.E1		69	34	29	12	26	5	4	FJ866141

Open in a separate window^adpi, days postinfection as defined by RNA testing ().^bVL, viral load on the sample date in which env genes were cloned.^cucPBMC, uncultured PBMCs; ccPBMC, cocultured PBMCs.^dAverage pairwise distance between the full-length env variants from a given subject. NA, not applicable because there was only one variant available from the subject.^eenv variants from these two subjects were cloned from >6 months postinfection, as noted, and should not be considered true early env variants.The full-length, functional env genes were sequenced and aligned to generate a maximum likelihood phylogenetic tree with reference sequences from the Los Alamos National Laboratory HIV database, as described previously (). Viral env clones from the same subject clustered together, and a wide spectrum of genetic diversity was observed overall (Fig. (Fig.1).1). Some women, such as subject QF495, were infected with a relatively homogeneous viral population, with average pairwise differences of only 0.12% between env variants (Table (Table11 and Fig. Fig.1).1). However, as observed previously in this cohort (, , , -), other individuals, such as subjects QH359 and QD435, were infected with more heterogeneous viral populations with average pairwise differences of 1.4% and 0.88% between variants, respectively (Table (Table11 and Fig. Fig.1).1). env genes from subtypes A (13 variants), C (3 variants), and D (8 variants), as well as A/D recombinants (4 variants) and A2/D recombinants (3 variants), were represented (Fig. (Fig.1).1). The viral subtypes were confirmed using the NCBI genotyping database (http://www.ncbi.nlm.nih.gov/).Open in a separate window FIG. 1.Maximum likelihood phylogenetic tree of full-length sequences from early subtype A, C, D, and A/D recombinant env variants in Kenya. The 31 novel env clones from Kenyan early infections and reference sequences for subtypes A, B, C, D, and K from the Los Alamos HIV database (http://www.hiv.lanl.gov/content/index) are displayed. The phylogenetic tree was rooted with subtype K env sequences. Values at nodes indicate the percentage of bootstraps in which the cluster the right was found; only values of 70% or greater are shown.The deduced amino acid sequences revealed that all functional variants had an uninterrupted open reading frame in env except for variant QB099.391I.ENV.C8, which had a frameshift mutation within the cytoplasmic tail of gp41. There was significant heterogeneity in the length of the protein variable loops, particularly V1/V2, which ranged from 57 amino acids (aa) to 113 aa (Table (Table1).1). The V3, V4, and V5 loops also varied in length, though less dramatically (Table (Table1).1). Variants from the same subject were generally similar in their variable-loop lengths. Moderate variation was also observed in the number and position of potential N-linked glycosylation sites (PNGS) (Table (Table11).Previous analyses indicated that early subtype C env proteins had shorter variable loops than did early subtype B env proteins (), suggesting that there are different env protein features between subtypes. Thus, to compare variable-loop lengths and the numbers of PNGS between subtypes using this expanded group of early env variants, we evaluated the 31 newly cloned variants plus an additional 15 subtype A variants (), 19 subtype B variants (), and 18 subtype C variants () from other early virus panels. In order to avoid bias, when more than one env variant was available from a subject, the average loop length or PNGS number for that subject''s env proteins was used. We did not observe significant differences in V1/V2 length, V5 length, or the numbers of PNGS between subtypes by the Kruskal-Wallis equality-of-populations rank test (Table (Table2)2) . However, there were significant differences between the V3 and V4 loop lengths of the subtypes after adjusting for multiple comparisons (Table (Table2).2). The differences in V3 length appeared to be a result of shorter V3 loops in subtype D env proteins than in early subtype B (P = 0.006) or C (P < 0.001) env proteins (Table (Table2).2). The differences in V4 length were caused by shorter V4 loops in subtype C env proteins in comparison to both subtype A and B env proteins (P < 0.001; Table Table22).

TABLE 2.

Summary of variable-loop lengths and the numbers of PNGS in gp120 and gp41 within early HIV-1 env variants^a

Parameter	Median value (25th percentile, 75th percentile) for subtype:				Kruskal- Wallis P value^b	Wilcoxon rank sum P values for individual comparisons^c
Parameter	A (n = 11)	B (n = 19)	C (n = 20)	D (n = 4)	Kruskal- Wallis P value^b	A vs. B	A vs. C	A vs. D	B vs. C	B vs. D	C vs. D
Length
V1/V2	70.3 (62, 76)	70 (66, 70)	65 (62, 76)	66.5 (62, 69)	0.21	0.730	0.282	0.215	0.051	0.113	0.846
V3	35 (34, 35)	35 (35, 35)	35 (34, 35)	34 (34, 35)	0.001	0.240	0.016	0.107	0.141	0.006	<0.001
V4	32 (30, 36)	33 (31, 34)	26.5 (22, 29)	29.5 (29, 31)	0.0001	0.880	<0.001	0.148	<0.001	0.023	0.056
V5	11 (11, 11)	10 (9, 11)	10 (9, 11)	11.5 (11, 12)	0.030	0.096	0.015	0.184	0.677	0.099	0.021
No. of PNGS in:
gp120	24 (23, 28)	25 (24, 26)	24 (23, 25)	26 (26, 27)	0.20	0.680	0.692	0.265	0.146	0.186	0.042
gp41	4 (4, 5)	5 (4, 5)	5 (4, 5)	4.5 (4, 5)	0.20	0.030	0.179	0.470	0.410	0.408	0.799
gp41ecto	4 (4, 4)	4 (4, 4)	4 (4, 5)	4 (4, 4)	0.044	0.107	0.025	0.550	0.088	0.507	0.201

Open in a separate window^aVariable-loop lengths and the numbers of PNGS in gp120 and gp41 within early HIV-1 env variants from subtypes A, B, C, and D characterized here and previously (, , ). n, number of samples.^bKruskal-Wallis equality-of-populations rank test (based on multiple comparisons; P values of <0.007 were considered significant; significant values are presented in boldface).^cWilcoxon rank sum test (based on multiple comparisons; P values of <0.008 were considered significant; significant values are presented in boldface).We then assessed the neutralization sensitivity of the pseudoviruses to antibodies in plasma from HIV-1-infected individuals and to HIV-1-specific MAbs by using the TZM-bl neutralization assay as described previously (, 23, ). Median inhibitory concentrations (IC₅₀s) were defined as the reciprocal dilution of plasma or concentration of MAb that resulted in 50% inhibition of infection (, ). The Kenya pool was derived by pooling plasma collected between 1998 and 2000 from 30 HIV-1-infected individuals in Mombasa, Kenya, and the other three pools were derived by pooling plasma collected between 1993 and 1997 from 10 individuals from Nairobi, Kenya, and with an infection with a known subtype (A, C, or D) of HIV-1 as described previously ().The env variants demonstrated a range of neutralization sensitivities to plasma samples, from neutralization resistant (defined as <50% neutralization with a 1:50 dilution of plasma) to neutralization sensitive with an IC₅₀ of 333 (Fig. (Fig.2).2). Some clones, such as QF495.23M.ENV.A1, were relatively sensitive to all the plasma pools, with IC₅₀s from 100 to 333, whereas other clones, such as QH343.21M.ENV.A10, were relatively resistant to these plasma pools, with IC₅₀s from <50 to 85 (Fig. (Fig.2).2). The plasma pools did differ in their neutralization potencies. The Kenya pool, with a median IC₅₀ of <50 across all viruses tested, was significantly less likely to neutralize these transmitted variants than were the subtype A, C, and D plasma pools, which had median IC₅₀s of 110, 105, and 123, respectively (P values of <0.0001, 0.0001, and 0.001, respectively, by paired t test on log-transformed IC₅₀s). The basis for these differences in neutralizing activity is not clear, although the location, timing, and level of immunodeficiency at the time of sample collection could have contributed to the differences in NAb levels between the pools.Open in a separate window FIG. 2.Neutralization sensitivity of early subtype A, C, D, and A/D recombinant env variants to plasma samples and MAbs in relation to the sequences of the MAb binding sites. The env used to generate the pseudovirus tested is shown at the left, and the plasma pool or MAb tested is indicated at the top. The IC₅₀s of each plasma sample or MAb against each viral pseudotype is shown, with darker shading indicating more potent neutralization, as defined at the bottom of the figure. Gray boxes indicate that <50% neutralization was observed at the highest dilution of plasma or concentration of MAb tested. Each IC₅₀ shown is an average of the results from two independent neutralization assays, using pseudovirus generated in independent transfection experiments. The median IC₅₀s from the 31 variants are shown at the bottom. Neutralization of the pseudovirus derived from the subtype B variant SF162 is shown as a control, and neutralizations of murine leukemia virus (MLV) and simian immunodeficiency virus clone 8 (SIV) are shown as negative controls. In the panels on the right, the sequences for the MAbs 2G12, 2F5, and 4E10 are displayed. For 2G12, the amino acid numbers for the five PNGS that are important for 2G12 binding are shown for each virus tested. A plus sign indicates that the PNGS at that site in the envelope sequence was preserved, and a minus sign indicates that the PNGS was deleted. A shift in the PNGS position is indicated by the amino acid position to which the PNGS shifted. All sequences were numbered relative to the HXB2 sequence. The two rightmost panels show data for the canonical 2F5 and 4E10 epitopes, with a period indicating that the amino acid is preserved.The env variants were significantly more susceptible to their subtype-matched plasma pool, with a higher mean IC₅₀ for subtype-matched plasma samples than for unmatched plasma samples (138 versus 108, P = 0.0081, paired t test). However, a significant amount of cross-subtype neutralization was observed, as every env variant that was susceptible to the subtype-matched plasma pool was also susceptible to at least one of the other plasma pools (Fig. (Fig.2).2). Thus, although potency was enhanced when the plasma antibodies were produced in response to infection with the same subtype of HIV-1, there were shared neutralization determinants between subtypes, as has been observed previously (reviewed in reference ).To identify potential correlates of neutralization sensitivity to the antibodies within these plasma pools, we included these 31 env variants and an additional 15 subtype A env variants we previously characterized from the same cohort with the same plasma pools (). We did not observe a change in neutralization sensitivity during the evolution of the HIV-1 epidemic in Kenya, as no correlation was observed between neutralization sensitivity and the calendar date from which the env variants were isolated. In addition, no correlation was observed between the neutralization sensitivity of a variant to the plasma pools and the duration of estimated infection within that individual. Finally, there was no significant correlation between the neutralization sensitivity and variable-loop length or the number of PNGS. Thus, although changes in the variable-loop length or number of PNGS may alter the exposure of epitopes within the HIV-1 env protein, these changes do not appear to be the primary determinant of neutralization sensitivity.Despite relatively universal sensitivity to at least one of the pooled plasma samples, these transmitted Kenyan env variants were generally resistant to the MAbs 2G12 (provided by Hermann Katinger, Polymun Scientific) and b12 (provided by Dennis Burton, The Scripps Research Institute), as well as 2F5 and 4E10 (obtained from the AIDS Research and Reference Reagent Program, National Institutes of Health) (Fig. (Fig.2),2), though these MAbs neutralized the subtype B env variant SF162, with IC₅₀s similar to those reported previously (). Subtype D strains were the most susceptible to MAbs, with 4/8 variants neutralized with <20 μg/ml of 2F5 and 2/8 neutralized with <20 μg/ml of the other MAbs. This could reflect the fact that subtype D variants are more closely related to subtype B strains (Fig. (Fig.1)1) (see reference ), and these MAbs were all derived from subtype B-infected individuals.Among all 31 variants, 2F5 was the most broadly neutralizing, with 15/31 variants from 8/14 subjects neutralized with <20 μg/ml of this MAb. Some 2F5-resistant env variants, such as QH209.14M.ENV.A2 and QB857.110I.ENV.B3, had mutations in the canonical 2F5 binding epitopes, though other 2F5-resistant env variants such as QF495.23M.ENV.A3 and QA790.204I.ENV.A4 maintained the canonical 2F5 epitope. The results with the MAb 4E10 were similar; 4E10 neutralized only seven variants from 4 of the 14 subjects, and the presence of mutations in the 4E10 epitope, which were common, did not predict neutralization sensitivity (Fig. (Fig.2).2). For instance, the env variants QH343.21M.ENV.A10 and QH343.21M.ENV.B5 contained identical N671S and D674S mutations and QH343.21M.ENV.B5 was highly sensitive to 4E10, while QH343.21M.ENV.A10 was resistant (Fig. (Fig.2).2). Thus, for the 2F5 and 4E10 epitopes, the presumed epitopes appear to be shielded in a subset of these early non-subtype B env variants, as has been previously observed (Fig. (Fig.2)2) (, , , ).The MAb b12 neutralized only two variants from two subtype D-infected individuals, with no neutralization of the subtype A, C, and A/D recombinant pseudoviruses. Only four variants from two subjects were neutralized by 2G12 at <20 μg/ml, and these were the only variants that maintained all five of the PNGS within the 2G12 epitope (Fig. (Fig.2).2). Overall, the median IC₅₀ of all the MAbs against these transmitted variants was >20 μg/ml. None of the variants was susceptible to all four MAbs (Fig. (Fig.2),2), unlike many of the early subtype B env variants characterized previously ().In summary, these newly characterized HIV-1 env clones represent a range of neutralization sensitivities and can be used to supplement existing panels of transmitted variants, in particular, adding the first subtype D and A/D recombinant variants. Some differences between subtypes in env structure following transmission were noted, though these differences did not correlate with neutralization sensitivity. Although the significant levels of cross-subtype neutralization sensitivity observed with plasma samples indicate that some neutralization determinants were shared across subtypes, the epitopes for the MAbs b12, 2G12, 2F5, and 4E10 did not appear to be among the shared determinants. Thus, despite the fact that significant attention has focused on using vaccination to develop antibodies that resemble these MAbs in their specificity, such antibodies may not neutralize the transmitted strains that are causing most new infections worldwide. These data therefore stress the importance of evaluating transmitted variants in endemic areas when designing immunogens and evaluating vaccine and microbicide strategies. 相似文献

15.

Intrathecal Humoral Responses Are Inversely Associated with the Frequency of Simian Immunodeficiency Virus Macrophage-Tropic Variants in the Central Nervous System

Elena Ryzhova Pyone Aye Tom Harvey Wei Cao Andrew Lackner Francisco González-Scarano 《Journal of virology》2009,83(16):8282-8288

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16.

Plasmid pAMS1-Encoded,Bacteriocin-Related “Siblicide” in Enterococcus faecalis

Christine M. Sedgley Don B. Clewell Susan E. Flannagan 《Journal of bacteriology》2009,191(9):3183-3188

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17.

Quantitative Real-Time PCR Assays for Sensitive Detection of Canada Goose-Specific Fecal Pollution in Water Sources

B. Fremaux T. Boa C. K. Yost 《Applied and environmental microbiology》2010,76(14):4886-4889

Canada geese (Branta canadensis) are prevalent in North America and may contribute to fecal pollution of water systems where they congregate. This work provides two novel real-time PCR assays (CGOF1-Bac and CGOF2-Bac) allowing for the specific and sensitive detection of Bacteroides 16S rRNA gene markers present within Canada goose feces.The Canada goose (Branta canadensis) is a prevalent waterfowl species in North America. The population density of Canada geese has doubled during the past 15 years, and the population was estimated to be close to 3 million in 2007 (4). Canada geese often congregate within urban settings, likely due to available water sources, predator-free grasslands, and readily available food supplied by humans (). They are suspected to contribute to pollution of aquatic environments due to the large amounts of fecal matter that can be transported into the water. This can create a public health threat if the fecal droppings contain pathogenic microorganisms (, , , , , , ). Therefore, tracking transient fecal pollution of water due to fecal inputs from waterfowl, such as Canada geese, is of importance for protecting public health.PCR detection of host-specific 16S rRNA gene sequences from Bacteroidales of fecal origin has been described as a promising microbial source-tracking (MST) approach due to its rapidity and high specificity (, ). Recently, Lu et al. () characterized the fecal microbial community from Canada geese by constructing a 16S rRNA gene sequence database using primers designed to amplify all bacterial 16S rRNA gene sequences. The authors reported that the majority of the 16S rRNA gene sequences obtained were related to Clostridia or Bacilli and to a lesser degree Bacteroidetes, which represent possible targets for host-specific source-tracking assays.The main objective of this study was to identify novel Bacteroidales 16S rRNA gene sequences that are specific to Canada goose feces and design primers and TaqMan fluorescent probes for sensitive and specific quantification of Canada goose fecal contamination in water sources.Primers 32F and 708R from Bernhard and Field () were used to construct a Bacteroidales-specific 16S rRNA gene clone library from Canada goose fecal samples (n = 15) collected from grass lawns surrounding Wascana Lake (Regina, SK, Canada) in May 2009 (for a detailed protocol, see File S1 in the supplemental material). Two hundred eighty-eight clones were randomly selected and subjected to DNA sequencing (at the Plant Biotechnology Institute DNA Technologies Unit, Saskatoon, SK, Canada). Representative sequences of each operational taxonomic unit (OTU) were recovered using an approach similar to that described by Mieszkin et al. (). Sequences that were less than 93% similar to 16S rRNA gene sequences from nontarget host species in GenBank were used in multiple alignments to identify regions of DNA sequence that were putatively goose specific. Subsequently, two TaqMan fluorescent probe sets (targeting markers designated CGOF1-Bac and CGOF2-Bac) were designed using the RealTimeDesign software provided by Biosearch Technologies (http://www.biosearchtech.com/). The newly designed primer and probe set for the CGOF1-Bac assay included CG1F (5′-GTAGGCCGTGTTTTAAGTCAGC-3′) and CG1R (5′-AGTTCCGCCTGCCTTGTCTA-3′) and a TaqMan probe (5′-6-carboxyfluorescein [FAM]-CCGTGCCGTTATACTGAGACACTTGAG-Black Hole Quencher 1 [BHQ-1]-3′), and the CGOF2-Bac assay had primers CG2F (5′-ACTCAGGGATAGCCTTTCGA-3′) and CG2R (5′-ACCGATGAATCTTTCTTTGTCTCC-3′) and a TaqMan probe (5′-FAM-AATACCTGATGCCTTTGTTTCCCTGCA-BHQ-1-3′). Oligonucleotide specificities for the Canada goose-associated Bacteroides 16S rRNA primers were verified through in silico analysis using BLASTN () and the probe match program of the Ribosomal Database Project (release 10) (). Host specificity was further confirmed using DNA extracts from 6 raw human sewage samples from various geographical locations in Saskatchewan and 386 fecal samples originating from 17 different animal species in Saskatchewan, including samples from Canada geese (n = 101) (Table (Table1).1). An existing nested PCR assay for detecting Canada goose feces () (targeting genetic marker CG-Prev f5) (see Table S1 in the supplemental material) was also tested for specificity using the individual fecal and raw sewage samples (Table (Table1).1). All fecal DNA extracts were obtained from 0.25 g of fecal material by using the PowerSoil DNA extraction kit (Mo Bio Inc., Carlsbad, CA) (File S1 in the supplemental material provides details on the sample collection).

TABLE 1.

Specificities of the CGOF1-Bac, CGOF2-Bac, and CG-Prev f5 PCR assays for different species present in Saskatchewan, Canada

Host group or sample type	No. of samples	No. positive for Bacteroidales marker:
Host group or sample type	No. of samples	CGOF1-Bac	CGOF2-Bac	CG-Prev f5	All-Bac
Individual human feces	25	0	0	1	25
Raw human sewage	6	0	0	0	6
Cows	41	0	0	0	41
Pigs	48	0	0	1	48
Chickens	34	0	0	8	34
Geese	101	58	51	59	95^a
Gulls	16	0	0	6	14
Pigeons	25	1	0	2	22
Ducks	10	0	0	0	10
Swans	1	0	0	0	1
Moose	10	0	0	0	10
Deer
White tailed	10	0	0	0	10
Mule	10	0	0	0	10
Fallow	10	0	0	0	10
Caribou	10	0	0	0	10
Bison	10	0	0	0	10
Goats	10	0	0	0	10
Horses	15	0	0	0	15
Total	392	59	51	77	381

Open in a separate window^aThe 6 goose samples that tested negative for the All-Bac marker also tested negative for the three goose markers.The majority of the Canada goose feces analyzed in this study (94%; 95 of 101) carried the Bacteroidales order-specific genetic marker designated All-Bac, with a relatively high median concentration of 8.2 log₁₀ copies g⁻¹ wet feces (Table (Table11 and Fig. Fig.1).1). The high prevalence and abundance of Bacteroidales in Canada goose feces suggested that detecting members of this order could be useful in identifying fecal contamination associated with Canada goose populations.Open in a separate window FIG. 1.Concentrations of the Bacteroidales (All-Bac, CGOF1-Bac, and CGOF2-Bac) genetic markers in feces from various individual Canada geese.The composition of the Bacteroidales community in Canada goose feces (n = 15) was found to be relatively diverse since 52 OTUs (with a cutoff of 98% similarity) were identified among 211 nonchimeric 16S rRNA gene sequences. Phylogenetic analysis of the 52 OTUs (labeled CGOF1 to CGOF52) revealed that 43 (representing 84% of the 16S rRNA gene sequences) were Bacteroides like and that 9 (representing 16% of the 16S rRNA gene sequences) were likely to be members of the Prevotella-specific cluster (see Fig. S2 in the supplemental material). Similarly, Jeter et al. () reported that 75.7% of the Bacteroidales 16S rRNA clone library sequences generated from goose fecal samples were Bacteroides like. The majority of the Bacteroides- and Prevotella-like OTUs were dispersed among a wide range of previously characterized sequences from various hosts and did not occur in distinct clusters suitable for the design of Canada goose-associated real-time quantitative PCR (qPCR) assays (see Fig. S2 in the supplemental material). However, two single Bacteroides-like OTU sequences (CGOF1 and CGOF2) contained putative goose-specific DNA regions that were identified by in silico analysis (using BLASTN, the probe match program of the Ribosomal Database Project, and multiple alignment). The primers and probe for the CGOF1-Bac and CGOF2-Bac assays were designed with no mismatches to the clones CGOF1 and CGOF2, respectively.The CGOF2-Bac assay demonstrated no cross-amplification with fecal DNA from other host groups, while cross-amplification for the CGOF1-Bac assay was limited to one pigeon fecal sample (1 of 25, i.e., 4% of the samples) (Table (Table1).1). Since the abundance in the pigeon sample was low (3.3 log₁₀ marker copies g⁻¹ feces) and detection occurred late in the qPCR (with a threshold cycle [C_T] value of 37.1), it is unlikely that this false amplification would negatively impact the use of the assay as a tool for detection of Canada goose-specific fecal pollution in environmental samples. In comparison, the nested PCR CG-Prev f5 assay described by Lu and colleagues () demonstrated non-host-specific DNA amplification with fecal DNA samples from several animals, including samples from humans, pigeons, gulls, and agriculturally relevant pigs and chickens (Table (Table11).Both CGOF1-Bac and CGOF2-Bac assays showed limits of quantification (less than 10 copies of target DNA per reaction) similar to those of other host-specific Bacteroidales real-time qPCR assays (, , ). The sensitivities of the CGOF1-Bac and CGOF2-Bac assays were 57% (with 58 of 101 samples testing positive) and 50% (with 51 of 101 samples testing positive) for Canada goose feces, respectively (Table (Table1).1). A similar sensitivity of 58% (with 59 of 101 samples testing positive) was obtained using the CG-Prev f5 PCR assay. The combined use of the three assays increased the detection level to 72% (73 of 101) (Fig. (Fig.2).2). Importantly, all markers were detected within groups of Canada goose feces collected each month from May to September, indicating relative temporal stability of the markers. The CG-Prev f5 PCR assay is an end point assay, and therefore the abundance of the gene marker in Canada goose fecal samples could not be determined. However, development of the CGOF1-Bac and CGOF2-Bac qPCR approach allowed for the quantification of the host-specific CGOF1-Bac and CGOF2-Bac markers. In the feces of some individual Canada geese, the concentrations of CGOF1-Bac and CGOF2-Bac were high, reaching levels up to 8.8 and 7.9 log₁₀ copies g⁻¹, respectively (Fig. (Fig.11).Open in a separate window FIG. 2.Venn diagram for Canada goose fecal samples testing positive with the CGOF1-Bac, CGOF2-Bac, and/or CG-Prev f5 PCR assay. The number outside the circles indicates the number of Canada goose fecal samples for which none of the markers were detected.The potential of the Canada goose-specific Bacteroides qPCR assays to detect Canada goose fecal pollution in an environmental context was tested using water samples collected weekly during September to November 2009 from 8 shoreline sampling sites at Wascana Lake (see File S1 and Fig. S1 in the supplemental material). Wascana Lake is an urban lake, located in the center of Regina, that is routinely frequented by Canada geese. In brief, a single water sample of approximately 1 liter was taken from the surface water at each sampling site. Each water sample was analyzed for Escherichia coli enumeration using the Colilert-18/Quanti-Tray detection system (IDEXX Laboratories, Westbrook, ME) () and subjected to DNA extraction (with a PowerSoil DNA extraction kit [Mo Bio Inc., Carlsbad, CA]) for the detection of Bacteroidales 16S rRNA genetic markers using the Bacteroidales order-specific (All-Bac) qPCR assay (), the two Canada goose-specific (CGOF1-Bac and CGOF2-Bac) qPCR assays developed in this study, and the human-specific (BacH) qPCR assay (). All real-time and conventional PCR procedures as well as subsequent data analysis are described in the supplemental material and methods. The E. coli and All-Bac quantification data demonstrated that Wascana Lake was regularly subjected to some form of fecal pollution (Table (Table2).2). The All-Bac genetic marker was consistently detected in high concentrations (6 to 7 log₁₀ copies 100 ml⁻¹) in all the water samples, while E. coli concentrations fluctuated according to the sampling dates and sites, ranging from 0 to a most probable number (MPN) of more than 2,000 100 ml⁻¹. High concentrations of E. coli were consistently observed when near-shore water experienced strong wave action under windy conditions or when dense communities of birds were present at a given site and time point.

TABLE 2.

Levels of E. coli and incidences of the Canada goose-specific (CGOF1-Bac and CGOF2-Bac), human-specific (BacH), and generic (All-Bac) Bacteroidales 16S rRNA markers at the different Wascana Lake sites sampled weekly^a

Site	E. coli			All-Bac			CGOF1-Bac			CGOF2-Bac			BacH
Site	No. of positive water samples/total no. of samples analyzed (%)	Min level-max level (MPN 100 ml⁻¹)	Mean level (MPN 100 ml⁻¹)	No. of positive water samples/total no. of samples analyzed (%)	Min level-max level (log copies 100 ml⁻¹)	Mean level (log copies 100 ml⁻¹)	No. of positive water samples/total no. of samples analyzed (%)	Min level-max level (log copies 100 ml⁻¹)	Mean level (log copies 100 ml⁻¹)	No. of positive water samples/total no. of samples analyzed (%)	Min level-max level (log copies 100 ml⁻¹)	Mean level (log copies 100 ml⁻¹)	No. of positive water samples/total no. of samples analyzed	Min level-max level (log copies 100 ml⁻¹)	Mean level (log copies 100 ml⁻¹)
W1	8/8 (100)	6-196	71.1	8/8 (100)	6.2-8.1	6.9	6/8 (75)	0-4.7	2.4	4/8 (50)	0-4	1.7	2/8	0-3.7	1.7
W2	9/10 (90)	0-1,120	194	10/10 (100)	5.8-6.8	6.4	9/10 (90)	0-3.7	2.6	8/10 (80)	0-3.3	2.2	0/10	0	0
W3	10/10 (100)	6-1,550	534	10/10 (100)	6-7.8	7	10/10 (100)	2.9-4.8	3.8	10/10 (100)	2-4.5	3.4	0/10	0	0
W4	10/10 (100)	16-1,732	529	10/10 (100)	6.4-7.6	7	10/10 (100)	3.2-4.6	3.9	10/10 (100)	2.8-4.3	3.4	0/10	0	0
W5	10/10 (100)	2-2,420	687	10/10 (100)	5.5-6.9	6.3	7/10 (70)	0-3.2	1.7	5/10 (50)	0-3.1	1.2	0/10	0	0
W6	10/10 (100)	3-1,990	389	10/10 (100)	5.5-7	6.3	9/10 (90)	0-4.3	2.8	6/10 (60)	0-5.1	2	1/10	0-3.4	1.3
W7	7/7 (100)	5-2,420	445	7/7 (100)	5.7-7.8	7	6/7 (86)	0-3.8	2.6	5/7 (71)	0-4.4	2.4	2/7	0-5.1	2.8
W8	10/10 (100)	17-980	160	10/10 (100)	6.3-8.6	7.1	8/10 (80)	0-4.6	2.8	7/10 (70)	0-4.4	2.3	0/10	0	0

Open in a separate window^aMin, minimum; max, maximum.The frequent detection of the genetic markers CGOF1-Bac (in 65 of 75 water samples [87%]), CGOF2-Bac (in 55 of 75 samples [73%]), and CG-Prev f5 (in 60 of 75 samples [79%]) and the infrequent detection of the human-specific Bacteroidales 16S rRNA gene marker BacH () (in 5 of 75 water samples [7%[) confirmed that Canada geese significantly contributed to the fecal pollution in Wascana Lake during the sampling period. Highest mean concentrations of both CGOF1-Bac and CGOF2-Bac markers were obtained at the sampling sites W3 (3.8 and 3.9 log₁₀ copies 100 ml⁻¹) and W4 (3.4 log₁₀ copies 100 ml⁻¹ for both), which are heavily frequented by Canada geese (Table (Table2),2), further confirming their significant contribution to fecal pollution at these particular sites. It is worth noting that concentrations of the CGOF1-Bac and CGOF2-Bac markers in water samples displayed a significant positive relationship with each other (correlation coefficient = 0.87; P < 0.0001), supporting the accuracy of both assays for identifying Canada goose-associated fecal pollution in freshwater.In conclusion, the CGOF1-Bac and CGOF2-Bac qPCR assays developed in this study are efficient tools for estimating freshwater fecal inputs from Canada goose populations. Preliminary results obtained during the course of the present study also confirmed that Canada geese can serve as reservoirs of Salmonella and Campylobacter species (see Fig. S3 in the supplemental material). Therefore, future work will investigate the cooccurence of these enteric pathogens with the Canada goose fecal markers in the environment. 相似文献

18.

Identification of a Polyketide Synthase Coding Sequence Specific for Anatoxin-a-Producing Oscillatoria Cyanobacteria

Sabrina Cadel-Six Isabelle Iteman Caroline Peyraud-Thomas Stéphane Mann Olivier Ploux Annick Méjean 《Applied and environmental microbiology》2009,75(14):4909-4912

相似文献

19.

Spontaneous Quinolone Resistance in the Zoonotic Serovar of Vibrio vulnificus

Francisco J. Roig A. Llorens B. Fouz C. Amaro 《Applied and environmental microbiology》2009,75(8):2577-2580

This work demonstrates that Vibrio vulnificus biotype 2, serovar E, an eel pathogen able to infect humans, can become resistant to quinolone by specific mutations in gyrA (substitution of isoleucine for serine at position 83) and to some fluoroquinolones by additional mutations in parC (substitution of lysine for serine at position 85). Thus, to avoid the selection of resistant strains that are potentially pathogenic for humans, antibiotics other than quinolones must be used to treat vibriosis on farms.Vibrio vulnificus is an aquatic bacterium from warm and tropical ecosystems that causes vibriosis in humans and fish (http://www.cdc.gov/nczved/dfbmd/disease_listing/vibriov_gi.html) (). The species is heterogeneous and has been subdivided into three biotypes and more than eight serovars (, 15, ; our unpublished results). While biotypes 1 and 3 are innocuous for fish, biotype 2 can infect nonimmune fish, mainly eels, by colonizing the gills, invading the bloodstream, and causing death by septicemia (). The disease is rapidly transmitted through water and can result in significant economic losses to fish farmers. Surviving eels are immune to the disease and can act as carriers, transmitting vibriosis between farms. Interestingly, biotype 2 isolates belonging to serovar E have been isolated from human infections, suggesting that serovar E is zoonotic (). This serovar is also the most virulent for fish and has been responsible for the closure of several farms due to massive losses of fish. A vaccine, named Vulnivaccine, has been developed from serovar E isolates and has been successfully tested in the field (). Although the vaccine provides fish with long-term protection from vibriosis, at present its use is restricted to Spain. For this reason, in many fish farms around the world, vibriosis is treated with antibiotics, which are usually added to the food or water.Quinolones are considered the most effective antibiotics against human and fish vibriosis (, , 31). These antibiotics can persist for a long time in the environment (), which could favor the emergence of resistant strains under selective pressure. In fact, spontaneous resistances to quinolones by chromosomal mutations have been described for some gram-negative bacteria (, , , , , 26). Therefore, improper antibiotic treatment of eel vibriosis or inadequate residue elimination at farms could favor the emergence of human-pathogenic serovar E strains resistant to quinolones by spontaneous mutations. Thus, the main objective of the present work was to find out if the zoonotic serovar of biotype 2 can become quinolone resistant under selective pressure and determine the molecular basis of this resistance.Very few reports on resistance to antibiotics in V. vulnificus have been published; most of them have been performed with biotype 1 isolates. For this reason, the first task of this study was to determine the antibiotic resistance patterns in a wide collection of V. vulnificus strains belonging to the three biotypes that had been isolated worldwide from different sources (see Table S1 in the supplemental material). Isolates were screened for antimicrobial susceptibility to the antibiotics listed in Table S1 in the supplemental material by the agar diffusion disk procedure of Bauer et al. (), according to the standard guideline (9). The resistance pattern found for each isolate is shown in Table S1 in the supplemental material. Less than 14% of isolates were sensitive to all the antibiotics tested, and more than 65% were resistant to more than one antibiotic, irrespective of their biotypes or serovars. The most frequent resistances were to ampicillin-sulbactam (SAM; 65.6% of the strains) and nitrofurantoin (F; 60.8% of the strains), and the least frequent were to tetracycline (12%) and oxytetracycline (8%). In addition, 15% of the strains were resistant to nalidixic acid (NAL) and oxolinic acid (OA), and 75% of these strains came from fish farms (see Table S1 in the supplemental material). Thus, high percentages of strains of the three biotypes were shown to be resistant to one or more antibiotics, with percentages similar to those found in nonbiotyped environmental V. vulnificus isolates from Asia and North America (, , ). In those studies, resistance to antibiotics could not be related to human contamination. However, the percentage of quinolone-resistant strains found in our study is higher than that reported in other ones, probably due to the inclusion of fish farm isolates, where the majority of quinolone-resistant strains were concentrated. This fact suggests that quinolone resistance could be related to human contamination due to the improper use of these drugs in therapy against fish diseases, as has been previously suggested (, ). Although no specific resistance pattern was associated with particular biotypes or serovars, we found certain differences in resistance distribution, as shown in Table Table1.1. In this respect, biotype 3 displayed the narrowest spectrum of resistances and biotype 1 the widest. The latter biotype encompassed the highest number of strains with multiresistance (see Table S1 in the supplemental material). Within biotype 2, there were differences among serovars, with quinolone resistance being restricted to the zoonotic serovar (Table (Table11).

TABLE 1.

Percentage of resistant strains distributed by biotypes and serovars

V. vulnificus	No. of isolates	Resistance distribution (%) for indicated antibiotic^a
V. vulnificus	No. of isolates	SAM	CTX	E	NAL	F	OT	OA	SXT-TMP	TE
Biotype 1	49	75.5	24.5	14.3	30.6	83.7	8.2	30.6	28.6	8.2
Biotype 2 (whole)	72	58.3	13.9	12.5	4.2	47.2	9.7	4.2	4.2	13.9
Biotype 2
Serovar E	36	30.3	12.1	3	9.1	27.3	15.2	9.1	3	21.2
Serovar A	23	100	9.1	18.2	0	77.3	0	0	9.1	4.6
Nontypeable	8	29	14.3	25	0	57.1	14.3	0	0	14.3
Serovar I	5	100	20	20	0	20	20	0	0	0
Biotype 3	5	100	0	20	0	80	0	0	0	20

Open in a separate window^aCTX, cefotaxime; E, erythromycin; OT, oxytetracycline; SXT-TMP, sulfamethoxazole-trimethoprim; TE, tetracycline.The origin of resistance to quinolones in the zoonotic serovar was further investigated. To this end, spontaneous mutants of sensitive strains were selected from colonies growing within the inhibition halo around OA or NAL disks. Two strains (strain CG100 of biotype 1 and strain CECT 4604 of biotype 2, serovar E) developed isolated colonies within the inhibition zone. These colonies were purified, and maintenance of resistance was confirmed by serial incubations on medium without antibiotics. Using the disk diffusion method, CG100 was shown to be resistant to SAM and F and CECT 4604 to F (see Table S1 in the supplemental material). The MICs for OA, NAL, flumequine (UB), and ciprofloxacin (CIP) were determined by using the microplate assay according to the recommendations of the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (8, ) and interpreted according to the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (). The MICs for OA and NAL and for the fluoroquinolones UB and CIP exhibited by the mutants and their counterparts are shown in Table Table2.2. The inhibition zone diameters correlated well with MICs (data not shown). Mutants FR1, FR2, FR3, and FR4 were resistant to NAL and sensitive to the remaining quinolones, although they showed higher resistances than their parental strains (Table (Table2).2). Thus, these four mutants showed increases of 32- to 128-fold for NAL MICs, 4- to 8-fold for UB MICs, and 16-fold for CIP MICs (Table (Table2).2). The fifth mutant, FR5, was resistant to the two tested quinolones and to UB, a narrow-spectrum fluoroquinolone. This mutant, although sensitive to CIP, multiplied its MIC for this drug by 128 with respect to the parental strain (Table (Table22).

TABLE 2.

MICs for quinolones and fluoroquinolones and mutations in gyrA, gyrB, and parC detected in naturally and artificially induced resistant strains

Strain(s)	MIC (μg ml⁻¹) for indicated antibiotic^b				Gene mutation^a
					gyrA				gyrB				parC
					Position		Codon change	aa change	Position		Codon change	aa change	Position		Codon change	aa change
	NAL	OA	UB	CIP	nt	aa	Codon change	aa change	nt	aa	Codon change	aa change	nt	aa	Codon change	aa change
CG100	0.5 (S)	0.125 (S)	0.0625 (S)	0.0078 (S)
FR1	16 (R)	1 (S)	0.25 (S)	0.125 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	NC	NC	NC	NC
FR2	16 (R)	1 (S)	0.25 (S)	0.125 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	NC	NC	NC	NC
CECT 4604	0.25 (S)	0.0625 (S)	0.0625 (S)	0.0078 (S)
FR3	32 (R)	2 (S)	0.5 (S)	0.125 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	NC	NC	NC	NC
FR4	32 (R)	2 (S)	0.5 (S)	0.125 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	NC	NC	NC	NC
FR5	256 (R)	16 (R)	16 (R)	1 (S)	248	83	AGT→ATT	S→I	1156	386	GCA→ACA	A→T	254	85	TCA→TTA	S→L
									1236	412	CAG→CAC	Q→H
CECT 4602	128 (R)	8 (R)	64 (R)	1 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	254	85	TCA→TTA	S→L
CECT 4603, CECT 4606, CECT 4608, PD-5, PD-12, JE	32 (R)	2 (S)	<1 (S)	<1 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	NC	NC	NC	NC
CECT 4862	64 (R)	2 (S)	2 (S)	<1 (S)	249	83	AGT→AGA	S→R	NC	NC	NC	NC	NC	NC	NC	NC
A2, A4, A5, A6, A7, PD-1, PD-3	64-128 (R)	2 (S)	4 (S)	<1 (S)	248	83	AGT→ATT	S→I	NC	NC	NC	NC	338	113	GCA→GTA	A→V
V1	128 (R)	4 (S)	4 (S)	<1 (S)	248	83	AGT→ATT	S→I	1274	425	GAG→GGG	E→G	NC	NC	NC	NC
									1314	438	AAC→AAA	N→K

Open in a separate window^aMutations in a nucleotide (nt) that gave rise to a codon change and to a change in amino acids (aa) are indicated. NC, no change detected.^bThe resistance (R) or sensitivity (S) against the antibiotic determined according to the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (9, ) is indicated in parentheses.For other gram-negative pathogens, quinolone resistance relies on spontaneous mutations in the gyrA, gyrB, parC, and parE genes that occur in a specific region of the protein known as the quinolone resistance-determining region (QRDR) (, , , , , 26, ). To test the hypothesis that mutations in these genes could also produce quinolone resistance in V. vulnificus, the QRDRs of these genes were sequenced in the naturally resistant strains and in the two sensitive strains that had developed resistances by selective pressure in vitro. The genomic DNA was extracted (3), and the QRDRs of gyrA, gyrB, parE, and parC were amplified using the primers shown in Table Table3,3, which were designed from the published genomes of biotype 1 strains YJ016 and CMCP6 (, ). PCR products of the predicted size were sequenced in an ABI 3730 sequencer (Applied Biosystems). Analysis of the QRDR sequences for gyrA, gyrB, parC, and parE of the mutants and the naturally resistant strains revealed that all naturally resistant strains, except one, shared a specific mutation at nucleotide position 248 with the laboratory-induced mutants (Table (Table2).2). This mutation gave rise to a change from serine to isoleucine at amino acid position 83. The exception was a mutation in the adjacent nucleotide that gave rise to a substitution of arginine for serine at the same amino acid position (Table (Table2).2). All the isolates that were resistant to the quinolone NAL had a unique mutation in the gyrA gene, irrespective of whether resistance was acquired naturally or in the laboratory (Table (Table2).2). This result strongly suggests that a point mutation in gyrA that gives rise to a change in nucleotide position 83 can confer resistance to NAL in V. vulnificus biotypes 1 and 2 and that this mutation could be produced by selective pressure under natural conditions. gyrA mutations consisting of a change from serine 83 to isoleucine have also been described in isolates of Aeromonas from water () and in diseased fish isolates of Vibrio anguillarum (26). Similarly, replacement of serine by arginine at amino acid position 83 in diseased fish isolates of Yersinia ruckeri () suggests that this mechanism of quinolone resistance is widespread among gram-negative pathogens. In all cases, these single mutations were also related to increased resistance to other quinolones (OA) and fluoroquinolones (UB and CIP) (Table (Table2),2), although the mutants remained sensitive according to the standards of the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (9, ). A total of 50% of the naturally resistant strains, all of them of biotype 1, showed additional mutations that affected parC (a change in amino acid position 113) or gyrB (changes in amino acids at positions 425 and 438) (Table (Table2).2). These strains exhibited higher MICs for OA and fluoroquinolones (Table (Table2),2), although they were still sensitive to these drugs (9, ). Finally, one isolate of biotype 2, serovar E, which was naturally resistant to quinolones and UB, showed a mutation in parC that gave rise to a substitution of leucine for serine at amino acid position 85 (Table (Table2).2). This mutation was shared only with the laboratory-induced mutant, also a biotype 2, serovar E mutant, which was resistant to the fluoroquinolone UB. The same mutation in parC had been previously described in diseased fish isolates of V. anguillarum that were highly resistant to quinolones (), but this had not been related to fluoroquinolone resistance in Vibrio spp. nor in other gram-negative bacteria. These results strongly suggest that resistance to fluoroquinolones in V. vulnificus is related to specific mutations in gyrA and parC and that mutations in different positions for parC or in gyrB could contribute to increased resistance to quinolones and fluoroquinolones. Our results also agree with previous studies confirming that the acquisition of higher quinolone resistance is more probable when arising from a gyrA parC double mutation than from a gyrA gyrB double mutation ().

TABLE 3.

Oligonucleotides used in this study

Primer	Sequence	Annealing temp (°C)	Size (bp)
GyrAF	GGCAACGACTGGAATAAACC	55.8	416
GyrAR	CAGCCATCAATCACTTCCGTC
ParCF	CGCAAGTTCACCGAAGATGC	56.6	411
ParCR	GGCATCCGCAACTTCACG
GyrBF	CGACTTCTGGTGACGATGCG	57.4	642
GyrBR	GACCGATACCACAACCTAGTG
ParEF	GCCAGGTAAGTTGACCGATTG	56.8	512
ParER	CACCCAGACCTTTGAATCGTTG

Open in a separate windowFinally, the evolutionary history for each protein was inferred from previously published DNA sequences of the whole genes from different Vibrio species after multiple sequence alignment with MEGA4 software () by applying the neighbor-joining method () with the Poisson correction (35). The distance tree for each whole protein showed a topology similar to the phylogenetic tree based on 16S rRNA analysis, with the two isolates of V. vulnificus forming a single group, closely related to Vibrio parahaemolyticus, Vibrio cholerae, V. anguillarum, and Vibrio harveyi (see Fig. S1A in the supplemental material). A second analysis was performed with the QRDR sequences of the different mutants and isolates of V. vulnificus (GenBank accession numbers FJ379836 to FJ379927) to infer the intraspecies relationships (see Fig. S1B in the supplemental material). This analysis showed that QRDRs of gyrA, gyrB, parC, and parE were highly homogeneous within V. vulnificus.In summary, the zoonotic serovar of V. vulnificus can mutate spontaneously to gain quinolone resistance, under selective pressure in vitro, due to specific mutations in gyrA that involve a substitution of isoleucine for serine at amino acid position 83. This mutation appears in biotype 2, serovar E diseased-fish isolates and biotype 1 strains, mostly recovered from fish farms. An additional mutation in parC, resulting in a substitution of lysine for serine at amino acid position 85, seems to endow partial fluoroquinolone resistance on biotype 2, serovar E strains. This kind of double mutation is present in diseased-fish isolates of the zoonotic serovar but not in resistant biotype 1 isolates, which show different mutations in gyrB or in parC that increase their resistance levels but do not make the strains resistant to fluoroquinolones. Thus, antibiotics other than quinolones should be used at fish farms to prevent the emergence and spread of quinolone resistances, especially to CIP, a drug widely recommended for human vibriosis treatment. 相似文献

20.

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

Vini Pereira David Waxman Adam Eyre-Walker 《Genetics》2009,183(4):1597-1600

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 (; ; ; ) and Pearson''s correlation coefficient (; ; ; ; ,; ; ). 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 , the mouse data from , and the human data from . 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:

Raw CEL files of gene expression levels were obtained from the NCBI Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/projects/geo/).
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).
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 (). 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., ; ; ), and in others the expression values are used without this transformation (e.g., ; ; ; ,; ; ). 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 window Figure 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 (): 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 window Figure 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

Data set	Statistic	Conserved uniform genes	Other genes	Wilcoxon test P-value
MAS5 normalization
Mouse–rat	Euclidean	1.66	2.79	<10⁻¹⁵
	Pearson	0.70	0.47	<10⁻¹⁵
Human–mouse	Euclidean	1.67	3.13	<10⁻¹⁵
	Pearson	0.78	0.58	<10⁻¹⁵
Human–rat	Euclidean	1.83	3.21	<10⁻¹⁵
	Pearson	0.78	0.58	<10⁻¹⁵
RMA normalization
Mouse–rat	Euclidean	0.59	1.40	<10⁻¹⁵
	Pearson	0.82	0.38	<10⁻¹⁵
Human–mouse	Euclidean	0.59	1.58	<10⁻¹⁵
	Pearson	0.81	0.48	<10⁻¹⁵
Human–rat	Euclidean	0.58	1.55	<10⁻¹⁵
	Pearson	0.73	0.50	<10⁻¹⁵

Open in a separate windowWe note that there are two additional advantages of the Euclidean distance. First, it can take into account differences in the absolute level of expression if those data are available, either because the method of assay allows this, for example, if ESTs, SAGE, sequencing, or RNA-Seq data are used, or because expression in the two species has been assessed on the same platform using probes that are conserved between the two species. Second, the square of the Euclidean distance is expected to increase linearly with time. have previously shown that the squared difference in log expression level increases linearly with time under a Brownian motion model of gene expression evolution. It is therefore expected that the squared Euclidean distance will increase with time since the squared Euclidean distance is the sum of the squared differences across tissues. We prove this in File S1; we also show that this linearity holds, approximately, when relative abundance values are used (see also ). 相似文献