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1.
A selection of interesting papers that were published in the two months before our press date in major journals most likely to report significant results in cell biology.  相似文献   

2.
The percentage of preservation of erythropoietic and granulopoietic precursor cells in the murine bone marrow was studied using in vitro methylcellulose clonal cell culture assays and in vivo murine spleen colony assays. This study clearly demonstrates
a. Type of Spleen Colonies Induced by 6-hr Postmortem Murine Bone Marrow Cellsa
Contents (chosen by)
525Cytoskeleton (Desai and Holleran)
526Cell regulation (Roche, Servant and Weiner)
528Nucleus and gene expression (Aasland and Weinzierl)
529Membranes and sorting (Ponnambalam)
530Membrane permeability (Slesinger)
531Cell-to-cell contact and extracellular matrix (Pfaff)
533Cell differentiation (van Roessel, Kaltschmidt, Tsang and Huckriede)
534Cell multiplication (Sclafani)
Mean (%)
Type of coloniest ScoreP ValueUnfrozenFrozen
Erythrocytic26.28314.1002.090.059
Granulocytic23.74132.9171.450.173
Mixed49.32152.7000.550.59
a
N = 92. the presence of pluripotent hemopoietic precursor cells in cryopreserved 0-, 3-, 6-, 9-, and 12-hr postmortem murine bone marrow cells. Apparently, the erythropoietic precursor cells are more sensitive to freezing injury as compared to granulopoietic precursor cells.
  相似文献   

3.
Recent development of titratable coions has paved the way for realizing all-atom molecular dynamics at constant pH. To further improve physical realism, here we describe a technique in which proton titration of the solute is directly coupled to the interconversion between water and hydroxide or hydronium. We test the new method in replica-exchange continuous constant pH molecular dynamics simulations of three proteins, HP36, BBL, and HEWL. The calculated pKa values based on 10-ns sampling per replica have the average absolute and root-mean-square errors of 0.7 and 0.9 pH units, respectively. Introducing titratable water in molecular dynamics offers a means to model proton exchange between solute and solvent, thus opening a door to gaining new insights into the intricate details of biological phenomena involving proton translocation.Solution pH is an important factor in biology. Although neutral pH in extracellular medium accounts for balanced electrostatics and proper folding of protein structures, pH gradients across cell membranes induce large conformational changes that are necessary for biological functions, such as ATP synthesis and efflux of small molecules out of the cell. To gain detailed insights into pH-dependent conformational phenomena, several constant pH molecular dynamics (pHMD) methods, based on either discrete or continuous titration coordinates, have been developed in the last decade (1–4). In the continuous pHMD (CpHMD) framework (2,4), a set of titration coordinates {λi} are simultaneously propagated along with the conformational degrees of freedom. Although the original CpHMD method based on the generalized Born (GB) implicit-solvent models (2,4) offers quantitative prediction of pKa values and pH dependence of folding and conformational dynamics of proteins (5), its accuracy and applicability to highly charged systems and those with dominantly hydrophobic regions are limited due to the approximate nature of the underlying implicit-solvent models.Motivated by the above-mentioned need, three groups have made efforts to develop a CpHMD method using exclusively the explicit-solvent models (6–8). In our development, the titration of acidic and basic sites is coupled with that of coions to level the total charge of the system (8). To further improve physical realism, here we replace the coions by titratable water molecules, which not only absorb the excess charge but also enable direct modeling of solute-solvent proton exchange in classical molecular dynamics simulations.To illustrate the utility of the new methodology, we applied it to the titration simulations of three proteins that were previously used to benchmark the GB-based CpHMD. Although this work does not explore specific interactions between titratable waters and proteins, the methodology can be further tested or improved to provide a rigorous way for modeling proton transfer in molecular dynamics, which is a computationally efficient alternative to the empirical valence-bond theory-based methodologies (9,10).We define titration of water as:
  • 1.Loss of a proton to give a negatively charged hydroxide,
H2O ? OH? + H+, (1)or
  • 2.Gain of a proton to give a positively charged hydronium,
H2O + H+ ? H3O+.(2)We now couple the titration of hydroxide (Eq. 1) with that of an acidic site of the solute in the CpHMD simulation,HA+OHKaA+H2O.(3)The use of hydronium is avoided here to prevent a potential artifact due to prolonged attraction with A. Analogously, we couple the titration of hydronium (Eq. 2) with that of a basic site,BH++H2OKbH3O++B.(4)Thus, effectively, a proton is transferred between the solute and solvent. However, we should note that in CpHMD simulations, titratable protons are represented by covalently attached dummies (2,4). Through varying the atomic charges and van der Waals interactions, they are seen by other atoms in the protonated state but not in the unprotonated state (see Table S1 in the Supporting Material). Furthermore, the solution proton concentration is implicitly modeled through a free energy term (2,4).In CpHMD, the reference potential of mean force (PMF) for titration is that of the model compound (blocked single amino acid in water) along λ (2,4). In the presence of cotitrating water molecules, it is necessary to add the PMF for the conversion of water to hydroxide or hydronium. One-nanosecond NPT simulations at ambient pressure and temperature were performed to calculate the average force, 〈dU/d,θ〉 at given θ-values, which are related to λ by λ = sin2 θ (see Fig. S1 in the Supporting Material). Thermodynamic integration was then applied to calculate the PMF. We found that the average force can be accurately fit when assuming the PMF is quadratic in λ (Fig. 1). The same applies to the PMFs for titration of models Asp, Glu, and His. After testing on the titration of model compounds (see Table S2), we performed 10-ns all-atom CpHMD simulations with the pH replica-exchange protocol for three proteins: HP36, BBL and HEWL (see the Supporting Material for details). Most of the calculated pKa values were converged in 10 ns per replica (see Fig. S3). Results are summarized in Fig. S4. Based on the 10-ns data, the root-mean-square (RMS) and average absolute errors are 0.9 and 0.7 pH units, respectively, while the largest absolute error is 2.5 (Glu35 of HEWL). Linear regression of the calculation versus experiment gives R2 of 0.8 and slope of 1.2.Open in a separate windowFigure 1Average force and potential of mean force for converting a water molecule to hydroxide (A) and hydronium. (B) (Data points) Average forces. (Dashed curves) Best fits using a linear function, 2A(λB). (Solid curves) Corresponding potential of mean force.

Table 1

Calculated and experimental pKa values of three proteins
ResidueExperimenta
GBa
All-atom CpHMD
Time (ns)b0–10–55–100–10
HP36
 Asp443.10 (0.01)3.2 (0.1)2.03.02.6 (0.5)
 Glu453.95 (0.01)3.5 (0.1)4.34.54.4 (0.1)
 Asp463.45 (0.12)3.5 (0.1)2.43.73.1 (0.6)
 Glu724.37 (0.03)3.5 (0.1)4.44.44.4 (0.0)
BBL
 Asp1293.88 (0.02)3.2 (0.0)2.23.22.7 (0.5)
 Glu1414.46 (0.04)4.3 (0.0)4.04.44.2 (0.2)
 His1426.47 (0.04)7.1 (0.0)5.95.85.8 (0.0)
 Asp1453.65 (0.04)2.8 (0.2)3.03.13.1 (0.0)
 Glu1613.72 (0.05)3.6 (0.3)4.23.94.0 (0.2)
 Asp1623.18 (0.04)3.4 (0.3)2.93.53.2 (0.3)
 Glu1644.50 (0.03)4.5 (0.1)5.74.65.2 (0.6)
 His1665.39 (0.02)5.4 (0.1)4.44.44.4 (0.0)
HEWL
 Glu72.6 (0.2)2.6 (0.1)3.63.43.5 (0.1)
 His155.5 (0.2)5.3 (0.5)5.15.15.1 (0.0)
 Asp182.8 (0.3)2.9 (0.0)2.53.32.9 (0.4)
 Glu356.1 (0.4)4.4 (0.2)8.58.78.6 (0.1)
 Asp481.4 (0.2)2.8 (0.2)−0.11.10.6 (0.6)
 Asp523.6 (0.3)4.6 (0.0)5.45.65.5 (0.1)
 Asp661.2 (0.2)1.2 (0.4)−0.60.80.3 (0.7)
 Asp872.2 (0.1)2.0 (0.1)0.82.11.5 (0.7)
 Asp1014.5 (0.1)3.3 (0.3)6.15.75.9 (0.2)
 Asp1193.5 (0.3)2.5 (0.1)3.03.33.2 (0.1)
Maximum absolute deviation1.82.42.62.5
Average absolute deviation (RMS deviation)0.5 (0.7)1.0 (1.2)0.6 (0.9)0.7 (0.9)
Linear fit R2 (slope)0.7 (0.8)0.8 (1.4)0.7 (1.1)0.8 (1.2)
Open in a separate windowaTaken from Wallace and Shen (12). The pKa''s of BBL were recalculated.bSampling time per pH replica.Breaking the simulations in two halves, we noticed that the second 5-ns sampling gave better agreement with experiment. The RMS deviation is reduced from 1.2 to 0.9 pH units, while the average absolute deviation is reduced from 1.0 to 0.6 pH units. The linear regression against experimental data is also improved, with the slope decreasing from 1.4 to 1.1 although R2 remains the same. Comparing these second-half results with the GB-based simulations, we find that the RMS and average absolute deviations are about the same as the GB-CpHMD results; however, the all-atom simulations show a small systematic overestimation (regression slope >1), whereas GB simulations show a systematic underestimation (regression slope <1).The improvement in the second halves of the simulations are seen mainly for residues involved in attractive electrostatic interactions, including Asp44 and Asp46 of HP36, Asp129 of BBL, and Asp48, Asp66, and Asp87 of HEWL. These residues are initially locked in salt-bridges or hydrogen bonds. However, in the second 5 ns, the attractive interactions weakened, leading to a decrease in the calculated pKa shifts relative to the model values and better agreement with experiment. For instance, Asp44 was initially in a salt-bridge distance from Arg55. However, the salt-bridge positions were sampled less often in the second 5 ns (see Fig. S5), which explains the 1-unit reduction in the calculated pKa shift. Significant fluctuation in ion-pair interactions was also observed in the work by Alexov (11). The carboxyl oxygen of Asp46 was a hydrogen-bond acceptor with both the backbone amide and hydroxyl of Ser43. These hydrogen bonds were less frequently sampled in the second 5 ns (see Fig. S6), leading to a decrease of the pKa shift for Asp46 by 1.3 units. These results indicate that extensive conformational sampling is necessary to give an accurate estimate of the ratio between the charged and neutral populations.Limited conformational sampling is also a contributing factor to the overestimation of the pKa shifts for buried residues (Fig. S7 and Fig. S8). The increase in SASA is correlated with the more frequent sampling of the states with λ close to 1, i.e., the deprotonated form (see Fig. S9). However, because Glu35 was buried in the starting conformation and the transition between buried and exposed states is slow compared to the simulation length, the exposed state may not be sufficiently sampled, leading to overestimation of the pKa shift.In contrast to Glu35, the SASA of Asp52 in HEWL is almost identical for both protonation states. The lack of conformational fluctuation is due to the strong hydrogen bonding with the side-chain amino group of Asn46 and Asn59 (data not shown). Overestimation of the pKa shifts for buried residues can also be attributed to the limitation of the additive force field which underestimates dielectric response in protein environment (more discussion see Supporting Material) of the pKa shifts for buried residues.Finally, to ascertain if the presence of hydroxide/hydronium introduces artifacts, we studied the interaction between hydroxide/hydronium and the titratable sites/ions. Comparing the hydroxide/hydronium with respective chloride/sodium ions, we find that the spatial distributions are nearly identical (see plots of distance distributions and radial distribution functions in Figs. S10–S13). However, the relative occupancy of the hydroxide around the neutral Asp/Glu, positive histidine, or sodium ion is 2–3 times as that of a chloride. The water-bridged interaction between sodium and chloride ions becomes much weaker when chloride is replaced by hydroxide or sodium is replaced by hydronium. By contrast, the occupancy of the hydronium around the solute is similar to that of the sodium. Furthermore, similar pKa results for these proteins were obtained when coions were used instead of titratable waters (data not shown). Thus, we believe that potential artifacts related to the ionized forms of water are negligible. Work is underway to further understand the limitations of the methodology and to explore applications to protein dynamics coupled to proton transfer.In summary, we have developed and tested titratable water models for use in all-atom CpHMD simulations. Although the benchmark pKa calculations indicate a comparable accuracy as the GB-CpHMD method, the all-atom method offers physical rigor and most importantly, it is applicable to systems that cannot be studied with GB-based simulations such as lipids and nucleic acids. We anticipate that the accuracy of this methodology can be further improved by incorporating the new-generation force fields that account for polarization. The coupling between proton titration of water and solute offers a computationally efficient way to model proton transfer in molecular mechanics simulations.  相似文献   

4.
5.
Cytosolic lipid droplets were considered until recently to be rather inert particles of stored neutral lipid. Largely through proteomics is it now known that droplets are dynamic organelles and that they participate in several important metabolic reactions as well as trafficking and interorganellar communication. In this review, the role of droplets in metabolism in the yeast Saccharomyces cerevisiae, the fly Drosophila melanogaster, and several mammalian sources are discussed, particularly focusing on those reactions shared by these organisms. From proteomics and older work, it is clear that droplets are important for fatty acid and sterol biosynthesis, fatty acid activation, and lipolysis. However, many droplet-associated enzymes are predicted to span a membrane two or more times, which suggests either that droplet structure is more complex than the current model posits, or that there are tightly bound membranes, particularly derived from the endoplasmic reticulum, which account for the association of several of these proteins.Cytosolic lipid droplets, originally thought to be simply coalesced neutral lipids waiting for lipolysis at metabolic demand, are now known to be considerably more complicated both structurally and functionally. There is general agreement that droplets are comprised of a core of neutral lipids, principally triglycerides and steryl esters, surrounded by a leaflet of phospholipids into which are embedded a specific subset of cellular proteins, the most abundant of which are members of the PAT family (see below) in animal cells (1). However, this model is probably too simple; there is evidence from physical probes of droplets isolated from yeast mutants unable to synthesize triglycerides or steryl esters that these two molecular families are partially segregated within the core, with thin shells of steryl esters forming concentric hollow spheres around an inner core composed principally of triglycerides (2).The next layer of complexity is the functional inhomogeneity of droplets. Subsets of droplets within the same cells exist with different populations of PAT proteins, differentiating among different sizes, ages, and levels of metabolic activity (3, 4). Perhaps most surprisingly, droplets may be comprised, at least in some cases, not of the layered core-phospholipid shell architecture at all but a knot of tightly woven endoplasmic reticulum (ER) surrounded by secreted neutral lipid, itself encased with a single leaflet. Such a model is based on electron microscopic thin sections (5), freeze fracture-immunogold evidence (6), immunohistochemical studies of ER luminal proteins within the droplet (7), and the identification of these proteins, notably ER chaperones, in several proteomic studies. Although certainly, such a complex structure must obey physical laws governing aqueous interactions with hydrophobic lipids and artifacts in processing for electron microscopy do occur, it may be best at present to keep an open mind and consider that droplets may not have the same structure among tissues and that they may take multiple physical forms in rapid order as they dynamically perform their functions.What are these functions? The most obvious one is lipid metabolism, namely the biogenesis and breakdown of the neutral lipids contained within the droplet. Although this conclusion predates proteomic studies (8), these recent studies have revealed the breadth and conservation of metabolic reactions that occur at or near the droplet surface, the subject of this review. Moreover, proteomics has demonstrated the surprising fact that droplets are likely to be very active in organellar communication because they are replete in rab proteins and other trafficking molecules. Our knowledge from proteomic studies of droplet trafficking and communication is discussed separately in this thematic review series.A major caveat must be kept in mind when evaluating droplet proteomics data: besides droplet trafficking through transient interactions with vesicles or target organelles such as early endosomes (9), droplets make extensive, tight, and long-lasting synapses with the endoplasmic reticulum, mitochondria, and peroxisomes (10, 11). The fact that ER, mitochondrial, peroxisomal, and a few plasma membrane proteins are found with such high frequency in the droplet proteome probably reflects these tight interorganellar interactions, perhaps similar to the mitochondrially associated membranes (MAMs) that link mitochondria with ER (12). The molecular basis for droplet-mediated synapses are not yet known. Besides the frequent occurrence of specific nondroplet organelle proteins in the droplet proteome, adventitious contamination of droplets is unlikely in view of the unique density of droplets that allow their flotation to the top of aqueous buffers and density gradients after centrifugation while all other cell components sink (which also permits several washes with high recovery), and the nonrandom coisolation of subsets of proteins from other organelles, such as the β-oxidation peroxisomal enzymes (10), which suggests specialized regions for metabolically-productive droplet interactions at the synapses.Droplet-ER interactions are a special case; it is the rule rather than the exception that enzymes of lipid metabolism that are found in the droplet proteome are also found to varying extents in the ER. This has been well documented in yeast through genome-wide green fluorescent protein (GFP)-tagging (13, 14). Erg6p, an enzyme in the latter part of the ergosterol biosynthetic pathway, is the only droplet protein in the pathway with a near-exclusive droplet localization in yeast; Erg1p, Erg7p, and Erg 27p are dually localized, and the pattern changes depending on metabolic state. Whether this general rule is specific for yeast, in which droplets remain on the ER surface (15), is not yet clear. However, several examples already exist in mammalian cells: cytochrome b5 reductase (DT diaphorase) and various sterol dehydrogenases (see 12).

TABLE 1.

Metabolic functions of droplets as revealed by proteomics
ProteinReference(s)Comments
Fatty Acid Synthesis
ATP citrate lyase(e)Generates acetyl-CoA
Acetyl-CoA carboxylase/ACC1(i) (j) (n) (o)(e)Generates malonyl CoA
3-Oxoacyl(ACP) synthase(e)Drosophila; early step in FA synthesis
Fatty acid synthase(e)Drosophila
Diaphorase 1/Cytochrome b5 reductase(g)(h)(j) (l) (n) (o)Redox carrier in FA elongation and many others
Fatty acid desaturase 2(e) (m)Many hydrophobic spans likely
Fatty Acid Activation
Acyl-CoA synthetase/ACSL1(g) (n)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL3(g)(h)(i) (j) (l) (n) (o)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL4(g)(h) (j) (l) (n)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL5(m)LACS2
Acyl-CoA synthetases/FAA1, FAA4, FAT1(a) (d)Yeast enzymes; FAT1 is a FA transporter; may have synthetase activity
Steroid Synthesis
Squalene epoxidase/ERG1(a) (i) (j) (o)(d)
Lanosterol synthase/ERG7(a)(g) (h) (i) (j) (m) (o)(d)
NAD(P) steroid dehydrogenase like (NSDHL)/ERG26(g)(h) (i)(m) (o)Sterol synthesis
3-keto reductase 17 βHSD7/ERG27(b)*(c)*(g) (j)(n) (o)(d)Sterol synthesis
C24-methyltransferase/ERG6(a) (c)* (d)Specific to ergosterol synthesis in fungi
17 β-HSD11 (retinal short chain dehydrogenase)(h) (i) (j) (l) (m) (n) (o) (e)Testosterone biosynthesis; steroid metabolism
17 β-HSD4(l)Bile salt snthesis
17 β-HSD13(m)A short-chain dehydrogenase
17 β-HSD3(m)Steroid metabolism
Triglyceride Synthesis
AcylDHAP reductase/AYR1(d)Determined early biochemically (68)
LysoPA acyltransferase/SLC1(d)Determined earlier biochemically (69)
DAG acyltransferase/DGA1Determined biochemically in yeast (70)
Lipolysis
Hormone-sensitive lipase(f)(g)Diglyceride lipase [first characterized in (71)]
Fat-specific gene 27(g)Lipase activity
ATGL(n) (o)Triglyceride lipase
Monoglyceride lipase(m)
Tgl3, Tgl4, Tgl5(a)Yeast triglyceride lipases [for Tgl4 and 5 see (60)]
Tgl1p, Yeh1p(a)Yeast steryl ester lipases; Yeh1 localized in (62)
PLC α(n)
Phospholipase A1(n)
Lipase Modulators
Perilipin(g)PAT family
ADRP(g)(h) (i) (k) (l) (m) (n) (o)PAT family
TIP47(g)(h) (l) (m) (o)PAT family
S3-12(g)PAT family
LSD2(e)(f)PAT family (Drosophila)
CGI-58(g) (i) (n) (o) (f)Regulator of ATGL; has endogenous acyltransferase activity (72)
Caveolin 1(g) (m) (n)May bridge perilipin with PKA to stimulate lipolysis
Other Redox Enzymes
Cytochrome p450(e)Mostly in ER
Cytochrome b5(e)Mostly in ER
Alcohol dehydrogenase 4(j) (m)(n) (e)Most in cytoplasm. Broad specificity, including retinols, aliphatic alcohols, and steroids
Aldehyde dehydrogenase /ALDH3B1(g)Can oxidize medium and long chain aldehydes
Glyceraldehyde phosphate dehydrogenase(a)(h) (l) (m) (n) (o) (e)Cytosolic glycolytic enzyme, but often found with droplets
Xanthine oxidoreductase(k)Identified in mammary tissue only
Gulonolactone oxidase(m)Drosophila; missing in humans. Role in ascorbic acid synthesis
Short-chain dehydrogenase/reductase member 1(g) (j) (n)(e)Unknown substrate
Other Enzymes
Acyl-CoA:ethanol o-acyltransferase /EHT1(a)(d)Generation of medium-chain ethyl esters
SCCPDH (CGI49)(h)(n) (o)Degradation of lysine
PI4 phosphatase/SAC1(n)
Serine palmitoyltransferase subunit 1 isoform a(n)Sphingolipid synthesis
SAM-dependent methyltransferase(j)Biosynthesis of phosphatidylcholine
Possible Contamination
Sterol carrier protein 2-related form(l) (e)May have thiolase activity. Peroxisomal contamination?
Palmitoyl-protein thioesterase(j) (n)Lysosomal contamination?
ER carboxyesterase(k)Mammary; used to make triglyc for lipooproteins
ATPsynthase2(g)Mitochondrial contamination
Carbamoyl P Synthetase 1(m)Mitochondrial contamination
Pyruvate carboxylase(g)(k)(e)Mitochondrial contamination?
Fatty acid translocase/CD36(g)Plasma membrane contamination?
Lipoprotein lipase (LPL)(g)Plasma membrane contamination
Open in a separate window*Non proteomics screens.(a) (29).*(b) (GFP screen) (13).*(c) (GFP screen) (14).(d) (10).(e) (73).(f) (74).(g) (23).(h) (75).(i) (76).(j) (24).(k) (77).(l) (78).(m) (79).(n) (40).(o) (5).The metabolic functions of droplets, as revealed or confirmed by proteomic studies, can be grouped into fatty acid synthesis and activation, sterol biosynthesis, triglyceride biosynthesis, and fatty acid mobilization from sterol esters and triglycerides. 相似文献   

6.
S Mironescu 《Cryobiology》1978,15(2):178-191
Correlated studies on volume distributions and cation (Na+ and K+) content of CHO cells in suspension were carried out after various exposures to hypertonic NaCl or sucrose (500–7550 mOsm in both the presence and absence of DMSO (5–20%; wv). The effects superimposed by ouabain (10?2–10?4m), amphotericin B (6–18 μg/ml), and glutaraldehyde (1.25%) on the above-mentioned parameters were also investigated. Volumetric analysis of CHO cells with the Coulter Channelyzer indicated a biphasic dose-dependent response to hypertonic media, the duration of the
TABLE 2. Correlation between Volume, Survival, and Cation Content of CHO Cells Exposed to Hypertonic Media in Suspension
  相似文献   

7.
8.

Background

Ophthalmic artery chemosurgery (OAC) for retinoblastoma was introduced by us 5 years ago for advanced intraocular retinoblastoma. Because the success was higher than with existing alternatives and systemic side effects limited we have now treated less advanced intraocular retinoblastoma (Reese-Ellsworth (RE) I-III and International Classification Retinoblastoma (ICRB) B and C).

Methodology/Principal Findings

Retrospective review of 5 year experience in eyes with Reese Ellsworth (
Osmolality mOsmExposure (min)Hypertonic agent
NaClSucrose
VaNa+K+SbVNa+K+S
100060 or lessSmallHighHighHighNormalLowHighHigh
1500–200060 or lessSmallHighLowLowNormalLowHighHigh
2000 or over60 or moreSmall or largecHighVery lowVery lowSmallVery lowVery lowVery low
Reese-Ellsworth (RE) Classification For Intraocular Retinoblastoma
GROUP I a. Solitary tumor, less than 4 disc diameters in size, at or behind the equator
b. Multiple tumors, none over 4 disc diameters in size, all at or behind the equator
GROUP II a. Solitary tumor, less than 4 to 10 disc diameters in size, at or behind the equator
b. Multiple tumors, none over 4 to 10 disc diameters in size, all at or behind the equator
GROUP III a. Any lesion anterior to the equator
b. Solitary tumors larger than 10 disc diameters behind the equator
GROUP IV a. Multiple tumors, some larger than 10 disc diameters
b. Any lesion extending anteriorly to the ora serrata
GROUP V a . Massive tumors involving over half the retina
b . Vitreous seeding
Open in a separate window

Table 2

International Classification for Retinoblastoma (ICRB) Scheme.
International Classification for Intraocular Retinoblastoma (ICRB)
Group A Small intraretinal tumors away from foveola and disc
* All tumors are 3 mm or smaller in greatest dimension, confined to the retina and * All tumors are located further than 3 mm from the foveola and 1.5 mm from the optic disc
Group B All remaining discrete tumors confined to the retina
* All other tumors confined to the retina not in Group A * Tumor-associated subretinal fluid less than 3 mm from the tumor with no subretinal seeding
Group C Discrete Local disease with minimal subretinal or vitreous seeding
* Tumor(s) are discrete * Subretinal fluid, present or past, without seeding involving up to ¼ retina * Local fine vitreous seeding may be present close to discrete tumor * Local subretinal seeding less than 3 mm (2DD) from the tumor
Group D Diffuse disease with significant vitreous or subretinal seeding
* Tumor(s) may be massive or diffuse * Subretinal fluid, present or past without seeding, involving up to total retinal detachment * Diffuse or massive vitreous disease may include “greasy” seeds or avascular tumor masses * Diffuse subretinal seeding may include subretinal plaques or tumor nodules
Group E Presence of any one or more of these poor prognosis features
* Tumor touching the lens * Tumor anterior to anterior vitreous face involving ciliary body or anterior segment * Diffuse infiltrating retinoblastoma * Neovascular glaucoma * Opaque media from hemorrhage * Tumor necrosis with aseptic orbital cellulites * Phthisis bulbi
Open in a separate window

Conclusions/Significance

Ophthalmic artery chemosurgery for retinoblastoma that was Reese-Ellsworth I, II and III (or International Classification B or C) was associated with high success (100% of treatable eyes were retained) and limited toxicity with results that equal or exceed conventional therapy with less toxicity.  相似文献   

9.
Nutrient input in streams alters the density and species composition of attached algal communities in open systems. However, in forested streams, the light reaching the streambed (rather than the local nutrient levels) may limit the growth of these communities. A nutrient‐enrichment experiment in a forested oligotrophic stream was performed to test the hypothesis that nutrient addition has only minor effects on the community composition of attached algae and cyanobacteria under light limitation. Moderate nutrient addition consisted of increasing basal phosphorus (P) concentrations 3‐fold and basal nitrogen (N) concentrations 2‐fold. Two upstream control reaches were compared to a downstream reach before and after nutrient addition. Nutrients were added continuously to the downstream reach for 1 year. Algal biofilms growing on ceramic tiles were sampled and identified for more than a year before nutrient addition to 12 months after. Diatoms were the most abundant taxonomic group in the three stream reaches. Nutrient enrichment caused significant variations in the composition of the diatom community. While some taxa showed significant decreases (e.g., Achnanthes minutissima, Gomphonema angustum), increases for other taxa (such as Rhoicosphenia abbreviata and Amphora ovalis) were detected in the enriched reach (for taxonomic authors, see Table 2 ). Epiphytic and adnate taxa of large size were enhanced, particularly during periods of favorable growth conditions (spring). Nutrients also caused a change in the algal chl a, which increased from 0.5–5.8 to 2.1–10.7 μg chl · cm?2. Our results indicate that in oligotrophic forested streams, long‐term nutrient addition has significant effects on the algal biomass and community composition, which are detectable despite the low light availability caused by the tree canopy. Low light availability moderates but does not detain the long‐term tendency toward a nutrient‐tolerant community. Furthermore, the effects of nutrient addition on the algal community occur in spite of seasonal variations in light, water flow, and water chemical characteristics, which may confound the observations.
Table 2. Percent abundances of the most frequent taxa in three reaches of the Fuirosos stream. U1 and U2 untreated; E, enriched both in the periods before (bef) and after (aft) the enrichment of the E reach. Acronyms identifying the taxa are indicated.
U1‐bef U1‐aft U2‐bef U2‐aft E‐bef E‐aft
Achnanthes biasolettiana Grunow ABIA 1.1 1.2 0.4 0.1 5.4 0.7
Achnanthes lanceolata (Bréb.) Grunow ALAN 7.2 1.3 5.7 7.1 7.3 2.2
Achnanthes minutissima Kütz. AMIN 56.2 55.0 81.2 71.4 52.2 34.5
Achnanthes lanceolata v. frequentissima Lange‐Bert. ALFR 0.0 0.1 0.1 0.9 1.0 0.0
Amphora inariensis Krammer AINA 1.9 2.0 0.3 0.1 1.0 1.4
Amphora ovalis (Kütz.) Kütz. AOVA 0.0 0.0 0.0 0.0 0.0 1.3
Amphora pediculus (Kütz.) Grunow APED 0.9 2.2 0.1 0.6 3.3 1.3
Cocconeis pediculus Ehrenb. CPED 0.1 0.2 0.0 0.1 0.2 1.7
Cocconeis placentula Ehrenb. CPLA 13.7 20.3 1.8 8.4 12.3 32.4
Cymbella silesiaca Bleisch in Rabenh. CSLE 0.0 0.2 0.0 0.1 0.0 0.1
Diploneis oblongella (Nägeli) Cleve‐Euler DOBL 0.6 0.0 0.9 0.2 0.0 0.0
Fragilaria capucina var. gracilis (Øestrup) Hustedt FCGP 0.3 1.0 0.1 0.0 0.1 3.5
Fragilaria capucina var. capitellata (Grunow) Lange‐Bert. FCCP 0.0 0.2 0.0 0.1 0.4 0.6
Fragilaria ulna (Nitzsch) Lange‐Bert. FULN 0.2 1.1 0.1 0.1 0.0 1.4
Gomphonema angustatum (Kütz.) Rabenh. GADI 1.6 0.6 1.6 1.8 1.0 0.8
Gomphonema angustum C. Agardh GANT 0.2 0.1 0.6 1.2 1.4 0.1
Gomphonema minutum (C. Agardh) C. Agardh GMIN 0.2 0.0 0.3 0.1 0.3 0.5
Gomphonema pumilum (Grunow) E. Reichardt et Lange‐Bert. GPUM 1.7 0.0 2.0 1.4 1.1 0.0
Meridion circulare (Grev.) C. Agardh MCIR 0.0 0.1 1.5 1.7 0.4 0.2
Navicula antonii Lange‐Bert. NANT 0.8 0.1 0.1 0.2 0.8 0.2
Navicula accomoda Hust. NARB 0.0 0.0 0.0 0.0 0.0 0.0
Navicula capitatoradiata H. Germ. NCPR 0.3 0.0 0.1 0.1 0.0 0.3
Navicula cryptocephala Kütz. NCRY 0.5 0.1 0.1 0.3 0.5 0.2
Nitzschia linearis (C. Agardh) W. Sm. NLIN 0.2 0.0 0.0 0.2 0.0 0.1
Nitzschia palea (Kütz.) W. Sm. NPAL 0.0 0.0 0.3 0.2 0.5 0.2
Reimeria sinuata (W. Greg.) Kociolek et Stoermer RSIN 3.4 2.0 0.6 1.2 4.9 2.8
Rhoicosphenia abbreviata (C. Agardh) Lange‐Bert. RABB 8.1 5.0 0.2 0.4 3.6 9.9

Citing Literature

Volume 44 , Issue 3 June 2008

Pages 564-572  相似文献   


10.
11.
The activity levels of DNA polymerases α and β have been measured by autoradiography in squash preparations from rat testis of sexually mature animals. Similar results were obtained with ‘fixed’ samples (dipped in acetone: ethanol for 5 min at 25 °C) or ‘unfixed’ samples (frozen in liquid nitrogen and freeze-dried). The activities of DNA polymerases α and β in situ were distinguished by differential assay conditions and by selective inhibition with compounds such as N-ethylmaleimide and aphidicolin. Using the endogenous chromatin as template, maximal activity for both enzymes was obtained in the presence of all four deoxyribonucleoside triphosphates, MgCl2 and ethylene glycol. When DNA polymerase activities in several predominant testicular cell types (pre-leptotene primary spermatocytes, pachytene primary spermatocytes, round spermatids and elongated spermatids) were quantitatively compared, on a per cell basis, the following percentage distribution was observed:
  相似文献   

12.
Hundreds of eukaryotic membrane proteins are anchored to membranes by a single transmembrane domain at their carboxyl terminus. Many of these tail-anchored (TA) proteins are posttranslationally targeted to the endoplasmic reticulum (ER) membrane for insertion by the guided-entry of TA protein insertion (GET) pathway. In recent years, most of the components of this conserved pathway have been biochemically and structurally characterized. Get3 is the pathway-targeting factor that uses nucleotide-linked conformational changes to mediate the delivery of TA proteins between the GET pretargeting machinery in the cytosol and the transmembrane pathway components in the ER. Here we focus on the mechanism of the yeast GET pathway and make a speculative analogy between its membrane insertion step and the ATPase-driven cycle of ABC transporters.The mechanism of membrane protein insertion into the endoplasmic reticulum (ER) has been extensively studied for many years (Shao and Hegde 2011). From this work, the signal recognition particle (SRP)/Sec61 pathway has emerged as a textbook example of a cotranslational membrane insertion mechanism (Grudnik et al. 2009). The SRP binds a hydrophobic segment (either a cleavable amino-terminal signal sequence or a transmembrane domain) immediately after it emerges from the ribosomal exit tunnel. This results in a translational pause that persists until SRP engages its receptor in the ER and delivers the ribosome-nascent chain complex to the Sec61 channel. Last, the Sec61 channel enables protein translocation into the ER lumen along with partitioning of hydrophobic transmembrane domains into the lipid bilayer through the Sec61 lateral gate (Rapoport 2007).Approximately 5% of all eukaryotic membrane proteins have an ER targeting signal in a single carboxy-terminal transmembrane domain that emerges from the ribosome exit tunnel following completion of protein synthesis and is not recognized by the SRP (Stefanovic and Hegde 2007). Nonetheless, because hydrophobic peptides in the cytoplasm are prone to aggregation and subject to degradation by quality control systems (Hessa et al. 2011), these tail-anchored (TA) proteins still have to be specifically recognized, shielded from the aqueous environment, and guided to the ER membrane for insertion. In the past five years, the guided-entry of TA proteins (GET) pathway has come to prominence as the major machinery for performing these tasks and the enabler of many key cellular processes mediated by TA proteins including vesicle fusion, membrane protein insertion, and apoptosis. This research has rapidly yielded biochemical and structural insights (and2)2) into many of the GET pathway components (Hegde and Keenan 2011; Chartron et al. 2012a; Denic 2012). In particular, Get3 is an ATPase that uses metabolic energy to bridge recognition of TA proteins by upstream pathway components with TA protein recruitment to the ER for membrane insertion. However, the precise mechanisms of nucleotide-dependent TA protein binding to Get3 and how the GET pathway inserts tail anchors into the membrane are still poorly understood. Here, we provide an overview of the budding yeast GET pathway with emphasis on mechanistic insights that have come from structural studies of its membrane-associated steps and make a speculative juxtaposition with the ABC transporter mechanism.

Table 1.

A catalog of GET pathway component structures
Pre-leptotene primary spermatocyte %Pachytene primary spermatocyte %Round spermatid %Elongated spermatid %
DNA polymerase α2542303
DNA polymerase β2934361
ComponentRole in the pathwayPDB ID
Sgt2Component of the pretargeting complex that delivers TA proteins to Get3; dimer interacts with Get4/Get5, contains TPR repeats that interact with Hsps3SZ7
Get5Component of the pretargeting complex that delivers TA proteins to Get3; dimer interacts with Get4 via amino-terminal domain and with Sgt2 via its ubiquitin-like domain2LNZ
3VEJ
2LO0
Get4Component of the pretargeting complex that delivers TA proteins to Get3; interacts with Get3 via amino-terminal domain and with Get4 via carboxy-terminal domain3LPZ
3LKU
3WPV
Get3ATPase that binds the TA protein; dimer interacts with the pretargeting complex in the cytosol, and with Get1/2 at the ER membraneTable 2
Get1ER receptor for Get3; integral ER membrane
protein, three TMDs; forms a complex with Get2
3SJA, 3SJB
3SJC, 3ZS8
3VLC, 3B2E
Get2ER receptor for Get3; integral ER membrane
protein, three TMDs; forms a complex with Get1
3SJD
3ZS9
Open in a separate windowTA, tail anchored; TPR, tetratricopeptide repeat; TMDs, transmembrane domains.

Table 2.

An itemized list of published Get3 structures with associated nucleotides and conformation nomenclature
OrganismNucleotideConformationPDB IDReferences
Get3
Schizosaccharomyces pombeNoneOpen2WOOMateja et al. 2009
Saccharomyces cerevisiaeNoneOpen3H84Hu et al. 2009
3A36Yamagata et al. 2010
Aspergillus fumigatusADPOpen3IBGSuloway et al. 2009
S. cerevisiaeADPOpen3A37Yamagata et al. 2010
Debaryomyces hanseniiADPClosed3IO3Hu et al. 2009
Chaetomium thermophilumAMPPNP-Mg2+Closed3IQWBozkurt et al. 2009
C. thermophilumADP-Mg2+Closed3IQXBozkurt et al. 2009
S. cerevisiaeADP•AlF4-Mg2+Fully closed2WOJMateja et al. 2009
Methanothermobacter thermautotrophicusADP•AlF4-Mg2+Fully closed3ZQ6Sherill et al. 2011
Methanococcus jannaschiiADP•AlF4-Mg2+Tetrameric3UG6Suloway et al. 2012
3UG7
Get3/Get2cyto
S. cerevisiaeADP-Mg2+Closed3SJDStefer et al. 2011
S. cerevisiaeADP•AlF4-Mg2+Closed3ZS9Mariappan et al. 2011
Get3/Get1cyto
S. cerevisiaeNoneSemiopen3SJCStefer et al. 2011
S. cerevisiaeADPSemiopen3VLCKubota et al. 2012
S. cerevisiaeNoneOpen3SJAStefer et al. 2011
3SJBStefer et al. 2011
3ZS8Mariappan et al. 2011
ADPOpen3B2EKubota et al. 2012
Open in a separate windowADP, adenosine diphosphate.  相似文献   

13.
14.
A commercially available tissue culture medium has been proven capable of preserving dog kidney function for at least 24 hr after simple cooling. The advantages of using tissue culture medium as preservation fluid instead of plasma or albumin solutions from the infectious and immunological points of view are obvious. An in vitro study was completed using the tissue
1.
  相似文献   

15.
Many plant species can be induced to flower by responding to stress factors. The short-day plants Pharbitis nil and Perilla frutescens var. crispa flower under long days in response to the stress of poor nutrition or low-intensity light. Grafting experiments using two varieties of P. nil revealed that a transmissible flowering stimulus is involved in stress-induced flowering. The P. nil and P. frutescens plants that were induced to flower by stress reached anthesis, fruited and produced seeds. These seeds germinated, and the progeny of the stressed plants developed normally. Phenylalanine ammonialyase inhibitors inhibited this stress-induced flowering, and the inhibition was overcome by salicylic acid (SA), suggesting that there is an involvement of SA in stress-induced flowering. PnFT2, a P. nil ortholog of the flowering gene FLOWERING LOCUS T (FT) of Arabidopsis thaliana, was expressed when the P. nil plants were induced to flower under poor-nutrition stress conditions, but expression of PnFT1, another ortholog of FT, was not induced, suggesting that PnFT2 is involved in stress-induced flowering.Key words: flowering, stress, phenylalanine ammonia-lyase, salicylic acid, FLOWERING LOCUS T, Pharbitis nil, Perilla frutescensFlowering in many plant species is regulated by environmental factors, such as night-length in photoperiodic flowering and temperature in vernalization. On the other hand, a short-day (SD) plant such as Pharbitis nil (synonym Ipomoea nil) can be induced to flower under long days (LD) when grown under poor-nutrition, low-temperature or high-intensity light conditions.19 The flowering induced by these conditions is accompanied by an increase in phenylalanine ammonia-lyase (PAL) activity.10 Taken together, these facts suggest that the flowering induced by these conditions might be regulated by a common mechanism. Poor nutrition, low temperature and high-intensity light can be regarded as stress factors, and PAL activity increases under these stress conditions.11 Accordingly, we assumed that such LD flowering in P. nil might be induced by stress. Non-photoperiodic flowering has also been sporadically reported in several plant species other than P. nil, and a review of these studies suggested that most of the factors responsible for flowering could be regarded as stress. Some examples of these factors are summarized in 1214

Table 1

Some cases of stress-induced flowering
Code (animal No.)Perfusion time (br)Perfusion pressure mm/HgFlow ml/minWeight gainpHpO2 mm/HgHistological appearance
12470-60 systolic96357.3150–180Grossly normal
22445-40 diastolic10830
3249630
44870-60 systolic80357.3150–180Grossly normal
54845-40 diastolic120407.4
64810040
77270-60 systolic115407.4150–180Slight vacuolization of the tubular cells
87245-40 diastolic9640
9728040
102470-60 systolic110357.3150–180Used for transplantation
112445-40 diastolic12035
122414040
132410030
14249630
Stress factorSpeciesFlowering responseReference
high-intensity lightPharbitis nilinduction5
low-intensity lightLemna paucicostatainduction29
Perilla frutescens var. crispainduction14
ultraviolet CArabidopsis thalianainduction23
droughtDouglas-firinduction30
tropical pasture Legumesinduction31
lemoninduction3235
Ipomoea batataspromotion36
poor nutritionPharbitis nilinduction3, 4, 13
Macroptilium atropurpureumpromotion37
Cyclamen persicumpromotion38
Ipomoea batataspromotion36
Arabidopsis thalianainduction39
poor nitrogenLemna paucicostatainduction40
poor oxygenPharbitis nilinduction41
low temperaturePharbitis nilinduction9, 12
high conc. GA4/7Douglas-firpromotion42
girdlingDouglas-firinduction43
root pruningCitrus sp.induction44
Pharbitis nilinduction45
mechanical stimulationAnanas comosusinduction46
suppression of root elongationPharbitis nilinduction7
Open in a separate window  相似文献   

16.
17.
The simian parvoviruses (SPVs) are in the genus Erythrovirus in the family Parvoviridae and are most closely related to the human virus B19. SPV has been identified in cynomolgus, rhesus, and pigtailed macaques. All of the primate erythroviruses have a predilection for erythroid precursors. Infection, which is common in macaques, is usually clinically silent. Disease from SPV is associated with immunosuppression due to infection with various retroviruses (SIV, simian retrovirus, and simian–human immunodeficiency virus), surgery, drug toxicity studies, and posttransplantation immunosuppressive treatment and therefore is of concern in studies that use parvovirus-positive macaques.Abbreviations: SHIV, chimeric simian–human immunodeficiency virus, SPV, simian parvovirus, SRV, type D simian retrovirus, SIV, simian immunodeficiency virusIn 1959, a small, nonenveloped virus (rat virus) was isolated from rat tissue cultures.21 Over the next decade, similar viruses were identified as the etiologic agents of severe enteritis in cats and mink and of facial abnormalities in newborn hamsters.44,45 The nonenveloped viruses are among the smallest viruses that infect mammalian cells, with a genome size of approximately 5 kb. They are named parvoviruses, from the Latin parvum, meaning small. The virion has icosahedral symmetry, is composed of 2 or 3 structural proteins (VP1, VP2, and sometimes VP3), and contains a linear single-stranded DNA genome. The viral DNA also encodes 1 or 2 nonstructural proteins (NS1 and NS2).Currently the family Parvoviridae is divided into 2 subfamilies, the Parvovirinae, which infect vertebrates, and the Densovirinae, invertebrates (2 Although many parvoviruses are associated with severe disease, others cause inapparent infections. The adeno-associated viruses are replication-defective parvoviruses, identified as contaminants of adenovirus stocks, and require coinfection with adenovirus or herpesvirus.

Table 1.

Parvovirus taxonomy
FamilySubfamilyGenusType speciesHosts
ParvoviridaeParvovirinaeParvovirusMinute virus of miceVertebrates
ErythrovirusB19 virus
DependovirusAdeno-associated virus 2
AmdovirusAleutian mink disease virus
BocavirusBovine parvovirus
DensovirinaeDensovirusJunonia coenia densovirusInvertebrates
IteravirusBombyx mori densovirus
BrevidensovirusAedes aegypti densovirus
PefudensovirusPeriplanta fuliginosa densovirus
Open in a separate windowA key feature of replication-competent, autonomous, parvoviruses is the requirement for mitotically active host cells for viral replication. Parvoviruses require a host cell to go through S phase to replicate and lack the ability to initiate host DNA synthesis in resting cells.34 The autonomous parvoviruses are fairly species-specific, although some will grow in cultured cells from other species. In addition, transformation of ordinarily nonpermissive cells can render them permissive for productive infection by rodent parvoviruses.40 However, parvoviruses vary markedly in host range and pathogenicity, as determined primarily by the capsid proteins.10 The most severe clinical effects tend to occur in fetal and newborn animals, including in utero death and congenital lesions.4 In older animals, clinical signs are due to lytic viral replication in target tissues and to the subsequent immune response4.  相似文献   

18.
Sulfonamide-resistant Escherichia coli and Salmonella isolates from pigs and chickens in Ontario and Québec were screened for sul1, sul2, and sul3 by PCR. Each sul gene was distributed differently across populations, with a significant difference between distribution in commensal E. coli and Salmonella isolates and sul3 restricted mainly to porcine E. coli isolates.Resistance to sulfonamides is frequent in bacteria from farm animals (7, 8, 9, 10) and is usually caused by the acquisition of the genes sul1, sul2, and sul3 (20, 22). The objectives of this study were (i) to assess the distribution of these genes in Escherichia coli and Salmonella enterica isolates in swine and chickens from two major provinces in Canada, (ii) to assess whether differences occur in the distribution of these genes among bacterial species found within two different animal host species, and (iii) to assess whether significant differences in the distribution of these genes are present between the commensal E. coli strains used as indicators for surveillance of antimicrobial resistance and the zoonotic Salmonella pathogens found in the same ecological niche. In contrast to previous studies, a multivariable logistic regression model was used to analyze the data, control for confounding factors, and assess the interaction effect between animal and bacterial species in terms of the probability of an isolate carrying a specific sul gene. The distribution of sulfonamide resistance genes among sulfonamide-resistant E. coli (393 isolates from chickens and 311 from swine) and Salmonella (13 isolates from chickens and 221 from swine) isolates was assessed. These isolates were collected by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) between 2003 and 2005 from ceca of apparently healthy animals at abattoirs in Ontario (n = 435) and Québec (n = 503). The methods used by CIPARS are presented in detail elsewhere (8-10). The isolates were screened with a previously published multiplex PCR for sul1, sul2, and sul3 (16). The sul1 and sul2 genes were found in E. coli and Salmonella isolates from both animal species. The sul3 gene was detected in both E. coli and Salmonella isolates from swine but only in E. coli isolates from chickens (Table (Table1).1). Three percent of the isolates had no detectable sul gene, 12.5% possessed two genes, and two isolates carried three genes (Table (Table1).1). Similar (2, 3, 14, 19) or higher (4, 11, 17) values for multiple genes have been reported by others. The overall higher prevalence of sul1 in Salmonella isolates and of sul2 and sul3 in E. coli isolates was in agreement with the results of previous studies (2-4, 11, 12, 14, 21).

TABLE 1.

Frequency of the three resistance genes sul1, sul2, and sul3 in sulfonamide-resistant E. coli and Salmonella isolates from chickens and swine in Ontario and Québec between 2003 and 2005
Bacterial speciesSource (no. of isolates)No. (%) of isolates with indicated gene(s)
sul1sul2sul3sul1 + sul2sul1+ sul3sul2 + sul3sul1 + sul2 + sul3None
E. coliSwine (311)61 (19.6)66 (21.2)132 (42.4)11 (3.5)9 (2.9)11 (3.5)2 (0.6)19 (6.1)
Chickens (393)103 (26.2)211 (53.7)9 (2.3)64 (16.3)0 (0.0)0 (0.0)0 (0.0)6 (1.5)
Total (704)164 (23.3)277 (39.3)141 (20.0)75 (10.7)9 (1.3)11 (1.6)2 (0.3)25 (3.6)
SalmonellaSwine (221)173 (78.3)20 (9.0)5 (2.3)7 (3.2)0 (0.0)14 (6.3)0 (0.0)2 (0.9)
Chickens (13)7 (53.8)5 (38.5)0 (0.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)1 (7.7)
Total (234)180 (76.9)25 (10.7)5 (2.1)7 (3.0)0 (0.0)14 (6.0)0 (0.0)3 (1.3)
Open in a separate windowThree logistic regression models (Table (Table2)2) for associations between the presence of each sul gene and the bacterial species, animal species, province of origin of the animals, and year of isolation were built using Stata 9 (StataCorp, College Station, TX). Statistical interactions between bacterial and animal species were assessed in the sul1 and sul2 models but not in the sul3 model (the sample size was insufficient). Tests were two tailed, with significance at a P value of ≤0.05. A significant interaction between bacterial and animal species was observed for sul1 (Table (Table2).2). The presence of statistical interactions indicates that the effect of each variable depends on the state of the other variable. Thus, the effects of bacterial and animal species on the distribution of sul1 cannot be interpreted independently. For instance, sul1 was more frequent in Salmonella isolates from swine than from chickens, but the reverse was true for E. coli isolates (Table (Table3).3). Genomic islands containing sul1 are present in some Salmonella serovars, including S. enterica serovar Typhimurium and S. Derby (1, 6, 18), which were the most frequent in the swine samples of this study (Table (Table4).4). This contrasts with E. coli strains, in which sul1 is usually associated with transposons and large transferable plasmids (22). Thus, the presence of significant statistical interactions in the sul1 model could be an indicator of the differential importance of horizontal gene transfer in E. coli strains versus clonal expansion of specific Salmonella serovars in the animal species investigated. No significant interaction was detected for sul2 (P = 0.66) (Table (Table2).2). This could be the consequence of the relatively small number of sul2-positive Salmonella isolates and the resultant lack of statistical power; however, it is also possible that sul2 has a different epidemiology than sul1. The sul2 gene has not been shown to be located on genomic islands and is usually plasmid borne (22). It may therefore be transferred more frequently than sul1 between E. coli and Salmonella and between bacteria from swine and chickens, leading to the absence of a significant interaction in the sul2 model. Serotyping, molecular typing, and assessment of gene transferability would be required to test these hypotheses.

TABLE 2.

Multivariable models for the distribution of the sul1, sul2, and sul3 sulfonamide resistance genes from swine and chickens at slaughter in Ontario and Québec between 2003 and 2005
Explanatory variable (referent group) or interactionOdds ratio (95% confidence interval)b
sul1sul2sul3
Salmonella (E. coli)1.54 (0.50-4.74)0.33 (0.21-0.51)0.10 (0.06-0.16)
Swine (chickens)0.49 (0.36-0.68)c0.16 (0.12-0.23)c39.57 (20.21-77.48)c
Québec (Ontario)0.80 (0.60-1.07)2.20 (1.61-2.99)c0.83 (0.56-1.23)
2004 (2003)0.77 (0.56-1.06)0.99 (0.71-1.40)1.84 (1.18-2.86)
2005 (2003)0.68 (0.45-1.04)1.36 (0.88-2.09)1.66 (0.99-2.79)
2005 (2004)0.88 (0.58-1.36)1.36 (0.88-2.11)0.90 (0.53-1.53)
Bacterial species × animal speciesa12.84 (3.79-43.46)cNINI
Open in a separate windowaStatistical interaction between data for bacterial species and animal species. The terms for this interaction are described in Table Table33.bExample of odds ratio interpretation: the odds of a porcine isolate carrying sul3 were 39.57 times higher than for an isolate obtained from chickens. NI, not included in the model. The sul2 model was not included because the P value for this interaction was equal to 0.66, and the sul3 model was not included because it did not converge when including this interaction.cP < 0.001.

TABLE 3.

Interaction terms for the association between bacterial and animal species for the presence of sul1
Contrast variablesOdds ratioa95% Confidence intervalP value
Salmonella in pigs vs Salmonella in chickens6.301.95-20.390.002
E. coli in pigs vs E. coli in chickens0.490.36-0.67<0.001
Salmonella in chickens vs E. coli in chickens1.540.50-4.740.451
Salmonella in pigs vs E. coli in pigs19.7912.28-31.87<0.001
Open in a separate windowaExample of interpretation: the interaction effect suggests that sul1-mediated sulfonamide resistance is 19.79 times more likely to occur in Salmonella in pigs than in E. coli in pigs.

TABLE 4.

Salmonella serovars and frequency of resistance genes detected in each serovar
Salmonella serovarNo. of isolatesaNo. of isolates with indicated genea
sul1sul2sul3
Agona14/014/00/00/0
Berta3/00/03/00/0
Bovismorbificans1/00/00/00/0
Brandenburg3/01/01/00/0
Derby63/059/04/02/0
Give O:15+1/01/00/00/0
Heidelberg2/40/30/02/0
4,12:−:−3/03/00/00/0
4,12:i:−2/02/00/00/0
4,5,12:−:−1/01/00/01/0
Rough-O:fg:−1/01/00/00/0
Rough-O:l,v:enz151/01/01/00/0
Infantis3/01/02/00/0
Johannesburg1/01/00/00/0
London2/01/00/01/0
Manhattan2/00/02/00/0
Mbandaka6/06/01/00/0
Ohio O:14+1/00/01/00/0
Putten1/01/00/00/0
Schwarzengrund0/60/10/50/0
Typhimurium110/3101/312/013/0
Total221/13194/727/519/0
Open in a separate windowaThe first and second numbers in each column represent swine and chicken isolates, respectively; the total number of tabulated occurrences of sulfonamide genes is larger than the number of resistant isolates investigated because some isolates carried several genes simultaneously.A significant increase in the prevalence of sul3 was detected between 2003 and 2004 (P = 0.007) but not between 2004 and 2005 (P = 0.055) nor at any time for sul1 and sul2 (Table (Table2).2). The sul3 gene has emerged recently (5), and its prevalence was probably still increasing in 2003. The odds of finding sul3 in Salmonella isolates were 10 times lower than for E. coli isolates and 40 times higher in swine than in chickens (Table (Table2).2). Other studies have also found high frequencies of sul3 in porcine E. coli isolates (5, 12, 20) and much lower frequencies in other sources (4, 11, 12, 14). Although these isolates were not typed, previous results have shown that sul3 is present in both pathogenic and commensal porcine E. coli isolates (5) of at least 13 different serotypes (P. Boerlin and R. M. Travis, unpublished data). The sul3 gene has spread extensively across porcine E. coli populations in North America and Europe but remains uncommon in other major farm animal species and in Salmonella populations (Table (Table1)1) (2, 13). It may require more time to spread to other populations. There may be biological and ecological barriers slowing its spread to bacteria of other animal species or coselection factors that favor its presence in porcine E. coli populations.Using the example of sulfonamides as a model for the application of multivariable statistical approaches to the study of antimicrobial resistance epidemiology, this study indicates that the relative frequencies of genes encoding resistance to the same antimicrobial either present in bacterial populations for decades or recently emerged can vary significantly between animal hosts and phylogenetically related bacterial species sharing the same ecological niche. Differences in the distribution of resistance determinants may remain hidden when assessing resistance phenotypes. Similar antimicrobial susceptibility results do not necessarily imply similar resistance genes. These findings highlight the need to further explore the interactions between commensals and pathogens and the ways in which commensal bacteria are interpreted as indicators of antimicrobial resistance in pathogens.  相似文献   

19.
20.
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