Automated methods to test connectedness and quantify indirectness of evidence in network meta-analysis.

Network meta-analysis compares multiple treatments from studies that form a connected network of evidence. However, for complex networks, it is not easy to see if the network is connected. We use simple techniques from graph theory to test the connectedness of evidence networks in network meta-analysis. The method is to build the adjacency matrix for a network, with rows and columns corresponding to the treatments in the network and entries being one or zero depending on whether the treatments have been compared or not, and with zeros along the diagonal. Manipulation of this matrix gives the indirect connection matrix. The entries of this matrix determine whether two treatments can be compared, directly or indirectly. We also describe the distance matrix, which gives the minimum number of steps in the network required to compare a pair of treatments. This is a useful assessment of an indirect comparison as each additional step requires further assumptions of homogeneity in, for example, design and target populations of included trials. If there are no loops in the network, the distance is a measure of the degree of assumptions needed; it is approximately this with loops. We illustrate our methods using several constructed examples and giving R code for computation. We have also implemented the techniques in the Stata package "network." The methods provide a fast way to ensure comparisons are only made between connected treatments and to assess the degree of indirectness of a comparison.

Simultaneous synthesis of treatment effects and mapping to a common scale: an alternative to standardisation.

OBJECTIVE: Trials often may report several similar outcomes measured on different test instruments. We explored a method for synthesising treatment effect information both within and between trials and for reporting treatment effects on a common scale as an alternative to standardisation STUDY DESIGN: We applied a procedure that simultaneously estimates a pooled treatment effect and the "mapping" ratios between the treatment effects on test instruments in a connected network. Standardised and non-standardised treatment effects were compared. The methods were illustrated in a dataset of 22 trials of selective serotonin reuptake inhibitors against placebo for social anxiety disorder, each reporting treatment effects on between one and six of a total nine test instruments. RESULTS: Ratios of treatment effects on different test instruments varied from trial to trial, with a coefficient of variation of 18% (95% credible interval 11-29%). Standardised effect models fitted the data less well, and standardised treatment effects were estimated with less relative precision than non-standardised effects and with greater relative heterogeneity. CONCLUSION: Simultaneous synthesis of treatment effects and mapping to a common scale make fewer assumptions than standardising by dividing effects by the sample standard deviation, allow results to be reported on a common scale, and deliver estimates with superior relative precision.

A polysaccharide extract of mulberry leaf ameliorates hepatic glucose metabolism and insulin signaling in rats with type 2 diabetes induced by high fat-diet and streptozotocin.

Mulberry leaf is a traditional medicine used to treat diabetes in the clinic. The aim of this study was to determine the mechanisms by which mulberry leaf polysaccharide (MLPII), improves hepatic glucose metabolism and insulin resistance in rats with type 2 diabetes induced by high fat and streptozotocin (STZ). MLPII was administered for 6 weeks after establishment of type 2 diabetes in Wistar rats. At the end of the experiment, oral glucose tolerance, liver glycogen content, glucose synthase (GS) activity and insulin resistance were determined. Expression patterns of proteins and genes associated with insulin signaling as well as biomarkers of oxidative stress and antioxidant enzyme activities were assayed. Compared with normal control rats, MLPII treatment significantly improved oral glucose tolerance (P < 0.01) and restored the glycogen level (P < 0.01) and GS activity (P < 0.05) in diabetic rats. Insulin resistance was improved in MLPII-treated diabetic rats (P < 0.01). Furthermore, expression levels of insulin receptor substrate 2 (IRS2), phosphatidylinositol 3-kinase (PI3K) and protein kinase B (PKB/AKT) involved in insulin signaling were significantly increased (P < 0.01), while protein­tyrosine phosphatase 1B (PTP1B) expression was markedly reduced (P < 0.01). The levels of 8-hydroxy-2-deoxyguanosine (8-OHdG) and malondialdehyde (MDA) in livers of the MLPII-treated group were significantly reduced (P < 0.01), while activities of the antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx), were significantly increased (P < 0.01, P < 0.01, P < 0.01, respectively). The results clearly indicate that MLPII treatment effectively normalizes hepatic glucose metabolism and insulin signaling by inhibiting the expression of PTP1B, activating the PI3K­AKT pathway and mitigating oxidative stress in the livers of rats with type 2 diabetes induced by high fat and STZ.

Purification, characterization and anti-diabetic activity of a polysaccharide from mulberry leaf.

In the present study, a high-purity polysaccharide from mulberry leaf (MLP) was purified and characterized, and its anti-diabetic effects were investigated in streptozotocin (STZ)-induced diabetic rats. Our results showed that the obtained MLP (purity 99.8%) was determined to be composed of d-arabinose, d-xylose, d-glucose, d-rhamnose and d-mannose with molar ratio of 1:2.13:6.53:1.04:8.73. Oral administration of MLP at 50-200mg/kgbodyweight daily for 5weeks significantly reduced the levels of fasting blood glucose (FBG), glycosylated serum protein (GSP), serum total cholesterol (TC), and serum triglyceride (TG), and increased the body weight, fasting insulin (FINS), C-peptide (C-P), liver glycogen, liver glucokinase, and serum high-density lipoprotein cholesterol (HDL-C). Moreover, MLP promoted marked pancreatic ß-cell regeneration and insulin secretion, and reduced liver fat accumulation in diabetic rats. The treatment effect of MLP on diabetes was similar to the effect of antidiabetic drug glibenclamide. These results clearly indicated that MLP may have a potential for the treatment of hyperglycemia and hyperlipidemia in diabetes.

Anti-diabetic effect of mulberry leaf polysaccharide by inhibiting pancreatic islet cell apoptosis and ameliorating insulin secretory capacity in diabetic rats.

Diabetes mellitus is a clinically complex disease characterized by chronic hyperglycemia with metabolic disturbances. In this study, we investigated the effect of mulberry leaf polysaccharide (MLPII) on pancreatic islet cell apoptosis and insulin secretory function in diabetic rats induced by a high fat diet and streptozotocin. Our results showed that MLPII treatment inhibited pancreatic islet cell apoptosis and ameliorated insulin secretory capacity of pancreatic ß-cells in diabetic rats. And further study demonstrated that chronic treatment of diabetic rats with MLPII resulted in up-regulation of anti-apoptotic B-cell leukaemia/lymphoma 2 (Bcl-2) protein and down-regulation of pro-apoptotic Bcl2-associated X (Bax) and caspase-3 protein in pancreatic islet cells. Moreover, MLPII significantly restored pancreatic duodenal homeobox-1 (PDX-1) protein nuclear localization, and increased mRNA and protein expression of PDX-1 and its downstream targets, glucose transporter 2 (GLUT2) and glucokinase (GCK) in pancreatic islet cells of diabetic rats. These findings suggested that MLPII might play a critical role in protecting pancreatic islet cell from apoptosis via elevation of Bcl-2/Bax ratio, and ameliorating insulin secretory capacity of pancreatic ß-cells via restoration of PDX-1 nuclear localization and expression levels in diabetic rats. This is the first report to explore the potential molecular mechanism involved in the hypoglycemic activity of the polysaccharide from mulberry leaves.

Simultaneous multioutcome synthesis and mapping of treatment effects to a common scale.

OBJECTIVES: A new method is presented for both synthesizing treatment effects on multiple outcomes subject to measurement error and estimating coherent mapping coefficients between all outcomes. It can be applied to sets of trials reporting different combinations of patient- or clinician-reported outcomes, including both disease-specific measures and generic health-related quality-of-life measures. It is underpinned by a structural equation model that includes measurement error and latent common treatment effect factor. Treatment effects can be expressed on any of the test instruments that have been used. METHODS: This is illustrated in a synthesis of eight placebo-controlled trials of TNF-α inhibitors in ankylosing spondylitis, each reporting treatment effects on between two and five of a total six test instruments. RESULTS: The method has advantages over other methods for synthesis of multiple outcome data, including standardization and multivariate normal synthesis. Unlike standardization, it allows synthesis of treatment effect information from test instruments sensitive to different underlying constructs. It represents a special case of previously proposed multivariate normal models for evidence synthesis, but unlike the former, it also estimates mappings. Combining synthesis and mapping as a single operation makes more efficient use of available data than do current mapping methods and generates treatment effects that are consistent with the mappings. A limitation, however, is that it can only generate mappings to and from those instruments on which some trial data exist. CONCLUSIONS: The method should be assessed in a wide range of data sets on different clinical conditions, before it can be used routinely in health technology assessment.

Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials.

Inconsistency can be thought of as a conflict between "direct" evidence on a comparison between treatments B and C and "indirect" evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.

Mapping from disease-specific to generic health-related quality-of-life scales: a common factor model.

OBJECTIVES: To develop a coherent method for estimating mappings between treatment effects on disease-specific measurement (DSM) instruments and generic health-related quality-of-life (QOL) measures, when both are subject to measurement errors. METHODS: We identified three properties that must be satisfied for mappings to be logically coherent: invertability, transitivity, and invariance to linear transformation. Of the common regressions, ordinary least squares (OLS), geometric mean (GM), and orthogonal regression, only GM has all these properties, and then only in special cases. We developed a common factor model of how DSM and generic QOL scales are related, and derived expressions for coherent mapping coefficients. We showed that these are equivalent to adjusted forms of OLS or GM regressions. Where cohort data are available on just one DSM and one QOL measure, external data on the reproducibility of the DSM are required. In some circumstances, the mappings can be estimated without external data. We illustrated the estimation of mapping coefficients by using data on EuroQol five-dimensional (EQ-5D) questionnaire, 12-item short form health survey (SF-12) Mental Component Summary, and the Beck Depression Inventory (BDI), from a trial of treatments for depression. RESULTS: OLS underestimates and GM overestimates mappings from DSMs to generic QOL measures. Mappings estimated by using external data on reliability were similar to those estimated by using internal data, suggesting approximate adequacy of the common factor model. CONCLUSIONS: Neither OLS nor GM regression, unless corrected, is suitable for estimating mappings between disease-specific and generic QOL scales. OLS systematically underestimates mappings, but it can be adjusted by using external information on test-retest reliability.

Which health-related quality-of-life outcome when planning randomized trials: disease-specific or generic, or both? A common factor model.

The primary outcomes in trials are usually disease-specific measures (DSMs) designed to be responsive to changes in the condition caused by treatment. For purposes of cost-effectiveness analysis, treatment effects on the DSM are often "mapped" into treatment effects on a generic health-related quality-of-life (QOL) scale, such as EuroQol five-dimensional questionnaire. Trialists have the option of including generic QOL measures as trial outcomes. We consider the relative efficiency (estimate divided by its standard error) of treatment effects derived from the DSM, the generic QOL, the generic QOL indirectly estimated from the mapped DSM, and a pooled estimate combining the direct and indirect information on the generic QOL. By using a "common factor" theory of the relationship between the DSM and the generic QOL, we define the circumstances under which indirectly estimated generic QOL is more efficient than the direct one and when a pooled QOL estimate is more efficient than the DSM estimate. As long as the DSM is more responsive, there is always a threshold sample size above which the indirect estimate has better precision than the direct estimate. This threshold, however, increases as the (1) relative responsiveness ratio of the DSM to the generic QOL increases, (2) precision of the estimated mapping coefficient increases, and (3) true effect becomes smaller. The pooled estimate on the generic QOL may be more efficient than the DSM itself unless the reliability of the DSM is particularly high. Trials powered on DSMs are likely to have sufficient power to detect treatment effect on the generic QOL if a pooled estimate is used. We conclude that generic QOL instruments should be routinely included in randomized controlled trials. Information on mapping coefficients and on relative responsiveness should be collected more systematically to facilitate both evidence synthesis and trial design.

Extending methods for investigating the relationship between treatment effect and baseline risk from pairwise meta-analysis to network meta-analysis.

Baseline risk is a proxy for unmeasured but important patient-level characteristics, which may be modifiers of treatment effect, and is a potential source of heterogeneity in meta-analysis. Models adjusting for baseline risk have been developed for pairwise meta-analysis using the observed event rate in the placebo arm and taking into account the measurement error in the covariate to ensure that an unbiased estimate of the relationship is obtained. Our objective is to extend these methods to network meta-analysis where it is of interest to adjust for baseline imbalances in the non-intervention group event rate to reduce both heterogeneity and possibly inconsistency. This objective is complicated in network meta-analysis by this covariate being sometimes missing, because of the fact that not all studies in a network may have a non-active intervention arm. A random-effects meta-regression model allowing for inclusion of multi-arm trials and trials without a 'non-intervention' arm is developed. Analyses are conducted within a Bayesian framework using the WinBUGS software. The method is illustrated using two examples: (i) interventions to promote functional smoke alarm ownership by households with children and (ii) analgesics to reduce post-operative morphine consumption following a major surgery. The results showed no evidence of baseline effect in the smoke alarm example, but the analgesics example shows that the adjustment can greatly reduce heterogeneity and improve overall model fit.

Effects of electromagnetic radiation on spatial memory and synapses in rat hippocampal CA1.

In this study, we investigated the effects of mobile phone radiation on spatial learning, reference memory, and morphology in related brain regions. After the near-field radiation (0.52-1.08 W/kg) was delivered to 8-week-old Wistar rats 2 hours per day for 1 month, behavioral changes were examined using the Morris water maze. Compared with the sham-irradiated rats, the irradiated rats exhibited impaired performance. Morphological changes were investigated by examining synaptic ultrastructural changes in the hippocampus. Using the physical dissector technique, the number of pyramidal neurons, the synaptic profiles, and the length of postsynaptic densities in the CA1 region were quantified stereologically. The morphological changes included mitochondrial degenerations, fewer synapses, and shorter postsynaptic densities in the radiated rats. These findings indicate that mobile phone radiation can significantly impair spatial learning and reference memory and induce morphological changes in the hippocampal CA1 region.

Automating network meta-analysis.

Mixed treatment comparison (MTC) (also called network meta-analysis) is an extension of traditional meta-analysis to allow the simultaneous pooling of data from clinical trials comparing more than two treatment options. Typically, MTCs are performed using general-purpose Markov chain Monte Carlo software such as WinBUGS, requiring a model and data to be specified using a specific syntax. It would be preferable if, for the most common cases, both could be derived from a well-structured data file that can be easily checked for errors. Automation is particularly valuable for simulation studies in which the large number of MTCs that have to be estimated may preclude manual model specification and analysis. Moreover, automated model generation raises issues that provide additional insight into the nature of MTC. We present a method for the automated generation of Bayesian homogeneous variance random effects consistency models, including the choice of basic parameters and trial baselines, priors, and starting values for the Markov chain(s). We validate our method against the results of five published MTCs. The method is implemented in freely available open source software. This means that performing an MTC no longer requires manually writing a statistical model. This reduces time and effort, and facilitates error checking of the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

[Effects of 2000 µW/cm2; electromagnetic radiation on expression of immunoreactive protein and mRNA of NMDA receptor 2A subunit in rats hippocampus].

AIM: To evaluate the effects of electromagnetic irradiation of 2000 µW/cm(2); exposure on mRNA and protein expression levels of immunoreactive protein and mRNA of NMDA receptor 2A subunit in rats hippocampal, and to explore the mechanism of electromagnetic irradiation induced learning and memory impairment. METHODS: Rats were randomly divided into normal control group, sham-radiated group, and 1 h/d, 2 h/d, and 3 h/d radiation groups. The rats in the radiation groups were fixed after microwave exposure of 2000 µW/cm(2);, then their learning and memory abilities were tested by Morris water maze experiment, the change of NR2A protein in hippocampal neurons of each group of rats were measured with immunohistochmistry and Western blot techniques, and the expression of NR2A mRNA in hippocampus were determined by RT-PCR. RESULTS: Compared with the normal control group, each index of the sham-radiated group has no significant change (P>0.05), while the latency of rats of radiated group in Morris water maze test were significantly longer (P<0.05). In the radiation group, the hippocampal neurons of rats showing evident reduction in the ratio of NR2A positive cells, irregular, and arrayed in disorder. Moreover, the expession of NR2A protein and its mRNA in hippocampal neurons were significant decreased (P<0.05). CONCLUSION: Electromagnetic irradiation of 2000 µW/cm(2); exposure can impair the learning and memory abilities of rats possibly through a mechanism correlated with the lower expression of NR2A protein and its mRNA in hippocampus.

Linear inference for mixed treatment comparison meta-analysis: A two-stage approach.

Mixed treatment comparisons (MTC) meta-analysis synthesises comparative evidence on multiple treatments or other interventions from a collection of randomised controlled trials (RCT) available in a research area, while still respecting the randomisation structure in RCTs. This paper sets out to examine the properties of MTC estimates and elucidate the concept of consistency between direct and indirect evidence in MTC networks. We decompose MTC synthesis into two stages. At the first stage, ordinary meta-analysis is performed in each group of trials that have the same treatment comparators-this provides the 'direct' estimates of relative effect parameters. At the second stage, the optimal consistent estimates that minimise the distance between the direct estimates and the consistency hyper-plane can be deduced as the weighted least squares solution to a linear regression model with a specific design matrix that represents the consistency conditions. The consistent MTC estimates can then be represented explicitly as linear combinations of direct estimates, and under normality assumptions the overall evidence consistency can be tested with a likelihood-ratio statistic. This two-stage framework further allows us to use the leverage statistics to diagnose influence of the first-stage evidence and use the regression residuals to assess local inconsistency. The method is illustrated with two examples from medical research. Copyright © 2011 John Wiley & Sons, Ltd.

Colonization of Morus alba L. by the plant-growth-promoting and antagonistic bacterium Burkholderia cepacia strain Lu10-1.

BACKGROUND: Anthracnose, caused by Colletotrichum dematium, is a serious threat to the production and quality of mulberry leaves in susceptible varieties. Control of the disease has been a major problem in mulberry cultivation. Some strains of Burkholderia cepacia were reported to be useful antagonists of plant pests and could increase the yields of several crop plants. Although B. cepacia Lu10-1 is an endophytic bacterium obtained from mulberry leaves, it has not been deployed to control C. dematium infection in mulberry nor its colonization patterns in mulberry have been studied using GFP reporter or other reporters. The present study sought to evaluate the antifungal and plant-growth-promoting properties of strain Lu10-1, to clarify its specific localization within a mulberry plant, and to better understand its potential as a biocontrol and growth-promoting agent. RESULTS: Lu10-1 inhibited conidial germination and mycelial growth of C. dematium in vitro; when applied on leaves or to the soil, Lu10-1 also inhibited the development of anthracnose in a greenhouse, but the effectiveness varied with the length of the interval between the strain treatment and inoculation with the pathogen. Strain Lu10-1 could survive in both sterile and non-sterile soils for more than 60 days. The strain produced auxins, contributed to P solubilization and nitrogenase activity, and significantly promoted the growth of mulberry seedlings. The bacteria infected mulberry seedlings through cracks formed at junctions of lateral roots with the main root and in the zone of differentiation and elongation, and the cells were able to multiply and spread, mainly to the intercellular spaces of different tissues. The growth in all the tissues was around 1-5 × 105 CFU per gram of fresh plant tissue. CONCLUSIONS: Burkholderia cepacia strain Lu10-1 is an endophyte that can multiply and spread in mulberry seedlings rapidly and efficiently. The strain is antagonistic to C. dematium and acts as an efficient plant-growth-promoting agent on mulberry seedlings and is therefore a promising candidate as a biocontrol and growth-promoting agent.

Modeling between-trial variance structure in mixed treatment comparisons.

In mixed treatment comparison (MTC) meta-analysis, modeling the heterogeneity in between-trial variances across studies is a difficult problem because of the constraints on the variances inherited from the MTC structure. Starting from a consistent Bayesian hierarchical model for the mean treatment effects, we represent the variance configuration by a set of triangle inequalities on the standard deviations. We take the separation strategy (Barnard and others, 2000) to specify prior distributions for standard deviations and correlations separately. The covariance matrix of the latent treatment arm effects can be employed as a vehicle to load the triangular constraints, which in addition allows incorporation of prior beliefs about the correlations between treatment effects. The spherical parameterization based on Cholesky decomposition (Pinheiro and Bates, 1996) is used to generate a positive-definite matrix for the prior correlations in Markov chain Monte Carlo (MCMC). Elicited prior information on correlations between treatment arms is introduced in the form of its equivalent data likelihood. The procedure is implemented in a MCMC framework and illustrated with example data sets from medical research practice.

Multiparameter evidence synthesis in epidemiology and medical decision-making.

Meta-analysis has been well-established for many years, but has been largely confined to pooling evidence on pair-wise contrasts. Broader forms of synthesis have also been described, apparently re-invented in disparate fields, each time taking different computational approaches. The potential value of Bayesian estimation of a joint posterior parameter distribution and simultaneously sampling from it for decision analysis has also been appreciated. However, applications have been relatively few in number, sometimes stylized, and presented mainly to a statistical methods audience. As a result, the potential for multiparameter evidence synthesis in both epidemiology and health technology assessment has remained largely unrecognized. The advent of flexible software for Bayesian Markov chain Monte Carlo in the shape of WinBUGS has the made these earlier strands of work more widely available. Researchers can now carry out synthesis at a realistic level of complexity. The Bristol programme has not only contributed to a growing body of literature on how to synthesize different evidence structures, but also on how to check the consistency of multiple information sources and how to use the resulting models to prioritize future research.

Biological control against bacterial wilt and colonization of mulberry by an endophytic Bacillus subtilis strain.

Forty-five bacterial isolates were collected from surface-sterilized leaves of mulberry (Morus alba L.). By screening their antagonistic activities against Ralstonia solanacearum in vitro, four isolates showed a remarkable inhibitory effect. The evaluation of the antagonistic strains against bacterial wilt of mulberry indicated that the strain Lu144 effectively reduced disease incidence. In the greenhouse, Lu144 displayed effective biological control against bacterial wilt of mulberry when it was applied to sterile or nonsterile soil before the infection by the pathogen. Based on bacteriological properties and 16S rRNA gene sequencing, Lu144 was identified as a strain of Bacillus subtilis. The endophytic population and infection process of Lu144 in mulberry seedlings was explored following recovery of the green fluorescent protein (GFP)-labeled Lu144 and examination of the labeled strain by confocal laser scanning microscopy. Interestingly, the infection of GFP-labeled Lu144 cells into the mulberry seedlings occurred through the cracks formed at the lateral root junctions and the zone of differentiation and elongation, and the cells were able to develop and transfer in mulberry and mainly in the intercellular spaces of different tissues. The population of the GFP-labeled Lu144 inoculant was larger and more stable in leaves than that in roots and stems.

[Identification and colonization of an antagonistic endophytic Burkholderia cepacia Lu10-1 isolated from mulberry].

OBJECTIVE: To identify and colonize an antagonistic bacterium, Lu10-1, isolated from the healthy mulberry. METHODS: Strain Lu10-1 was identified based on the analysis of its 16S rRNA gene sequence homology, the physiological and biochemical characteristics, and the recA gene sequence comparison. A spontaneous Lu10-1 mutant tolerant to rifampicin and ampicillin were isolated by gradually increasing the concentration of the two antibiotics. The mutants were used to assess the ability of Lu10-1 to colonize mulberry by different inoculation approaches, including stem and leaf acupuncturing, seed soaking, root soaking and leaf daubing. RESULTS: Lu10-1 belonged to Burkholderia. In the phylogenetic tree, Lu10-1 was the closest relative to B. cepacia (X80284) with more than 98% sequences similarity. The 16S rDNA sequences of Lu10-1 have been registered at GenBank database under the accession number EF546394. Moreover, our results also indicated that the population of strain Lu10-1 living in the mulberry tissues decreased as a whole after the treatment of seed soaking. The bacterial density inside the mulberry seedling tissues decreased to a steady level 20 days after germination. The population of strain Lu10-1 in mulberry leaves and stems after the treatment of root soaking increased first and then decreased. CONCLUSION: The strain Lu10-1 fell into Burkholderia cepacia genomovar I as a single species. Furthermore, the strain Lu10-1 could colonize and transmit in mulberry, while its resistance to plant pathogen was not changed during the process of colonization compared to the original strains. Taken together, we suggest that Burkholderia. cepacia Lu10-1 will play an important role in the biological control of mulberry disease.