Comparative study on the antibiotic susceptibility and plasmid profiles of Vibrio alginolyticus strains isolated from four Tunisian marine biotopes

The antibiotic resistance patterns and the plasmids profiles of the predominant etiological agent responsible for vibriosis in Tunisia, V. alginolyticus were studied to contribute to control their spread in some Mediterranean aquaculture farms and seawater. The sixty-nine V. alginolyticus strains isolated from different marine Tunisian biotopes (bathing waters, aquaculture and conchylicole farms and a river connected to the seawater during the cold seasons) were multi-drug resistant with high resistance rate to ampicillin, kanamycin, doxycyclin, erythromycin, imipinem, and nalidixic acid. The multiple resistance index ranged from 0.3 to 0.7 for the isolates of Khenis, from 0.5 to 0.8 for those of Menzel Jmil, from 0.5 to 0.75 (Hergla) and from 0.3 to 0.7 for the isolates of Oued Soltane. The high value of antibiotic resistance index was recorded for the V. alginolyticus population isolated from the fish farm in Hergla (ARI?=?0.672) followed by the population isolated from the conchylicole station of Menzel Jmil (ARI?=?0.645). The results obtained by the MIC tests confirmed the resistance of the V. alginolyticus to ampicillin, erythromycin, kanamycin, cefotaxime, streptomycin and trimethoprim. Plasmids were found in 79.48?% of the strains analyzed and 30 different plasmid profiles were observed. The strains had a high difference in the size of plasmids varying between 0.5 and 45?kb. Our study reveals that the antibiotic-resistant bacteria are widespread in the aquaculture and conchylicole farm relatively to others strains isolated from seawater.     

Detection and characterization of Shigella species isolated from food and human stool samples in Nabeul, Tunisia, by molecular methods and culture techniques

Aims: This study was designed to isolate Shigella spp. strains from food and stool samples by a combination of PCR and culture methods and characterize their serotypes, antibiotic resistance profiles, virulence genes and pulsed‐field gel electrophoresis (PFGE) patterns to investigate possible clonal relationships amongst strains circulating. Methods and Results: Six Shigella spp. strains were isolated from 280 food samples against 16 Shigella isolates from 236 stool samples of symptomatic patients and asymptomatic food handlers during the period from January 2007 to December 2009 in Public Health Regional Laboratory of Nabeul. The detection of ipaH, ipaBCD, ial, ShET‐1 and ShET‐2 was performed by a PCR technique with specific primers. Conclusions: The use of PCR technique improved the rate of detecting Shigella in stool samples from 6·7 to 14% and in food samples from 2·1 to 8·6%. Percentage of Shigella isolates and ipaH‐specific PCR demonstrated a marked pattern of seasonality, increasing in summer and fall seasons for human and food isolates. Amongst the environmental strains, 50% of isolates were invasive. However, for the 16 clinical strains isolated, nine were found to be positive for both ial and ipaBCD gene and 11 were found to produce ShET‐1 and/or ShET‐2. XbaI PFGE analysis revealed the presence of a predominant clone amongst Shigella sonnei strains recovered from different sources circulating in Nabeul, Tunisia, throughout the years 2007–2009. Significance and Impact of the Study: This study demonstrated the existence of Shigella in food samples and dispersion of different virulence genes amongst these isolates, which appear to constitute an environmental source of epidemic spread. The clonal relationships amongst strains isolated from food elements and human stools indicate the incrimination of different kinds of foods as vehicle of transmission of Shigella, which are usually escaped from detection by traditional culture methods.     

Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method

We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein.     

Bacterial community diversity assessment in municipal solid waste compost amended soil using DGGE and ARISA fingerprinting methods

Bacterial community structure and diversity of Tunisian agricultural soil treated with different amounts of municipal solid waste compost (MSWC) and other fertilizers were studied using DGGE and ARISA fingerprinting methods. Sequence analysis of dominant DGGE bands revealed the presence of three major clusters, Cytophaga/Flexibacter/Bacteroides (CFB) group, Proteobacteria and Acidobacteria group. Using ARISA profiles, dominant populations were assigned to low and high GC Gram positive bacteria, Cyanobacteria, Spirochetes and Cytophagales. The two methods revealed the absence of significant bacterial community shifts related to the different MSWC applications. Moreover, indigenous bacterial population of the used loam-clayey soil was observed to limit proliferation and survival of Proteobacteria, initially dominant in MSWC and farmyard manure. Effectiveness of the two methods for soil bacterial community studying was shown. While DGGE was more accurate for bacterial identification, ARISA was more practical for handling and rapid estimation of dominant bacteria.     

Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel

Key message

Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement.

Abstract

So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F₅–F₇ lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r ² = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.

     

Quercetin protects HCT116 cells from Dichlorvos-induced oxidative stress and apoptosis

The present study was designed to assess the possible protective effects of Quercetin (QUER), a flavonoid with well-known pharmacological effects, against Dichlorvos (DDVP)-induced toxicity in vitro using HCT116 cells. The cytotoxicity was monitored by cell viability, reactive oxygen species (ROS) generation, anti-oxidant enzyme activities, malondialdehyde (MDA) production, and DNA fragmentation. The apoptosis was assessed through the measurement of the mitochondrial transmembrane potential (ΔΨm) and caspase activation. The results indicated that pretreatment of HCT116 cells with QUER, 2 h prior to DDVP exposure, significantly decreased the DDVP-induced cell death, inhibited the ROS generation, modulated the activities of catalase (CAT) and superoxide dismutase (SOD), and reduced the MDA level. The reductions in mitochondrial membrane potential, DNA fragmentation, and caspase activation were also attenuated by QUER. These findings suggest that dietary QUER can protect HCT116 cells against DDVP-induced oxidative stress and apoptosis.     

Typing of Staphylococcal Cassette Chromosome <Emphasis Type="Italic">mec</Emphasis> Encoding Methicillin Resistance in <Emphasis Type="Italic">Staphylococcus aureus</Emphasis> Strains Isolated at the Bone Marrow Transplant Centre of Tunisia

Staphylococcal Cassette Chromosome mec (SCCmec) is a mobile genetic element that carries the gene mecA mediating the methicillin resistance in staphylococci. It is composed of mec and ccr gene complexes. Six SCCmec types have been defined so far. SCCmec typing of 13 methicillin-resistant Staphylococcus aureus (MRSA) out of 72 (18%) non redundant S. aureus strains recovered in 1998–2007 at the Bone Marrow Transplant Centre of Tunis was carried out. The isolates were identified by conventional methods. Antibiotic susceptibility was determined by oxacillin and cefoxitin disks and oxacillin MIC by E-test. Methicillin resistance was detected by mecA PCR. The SCCmec complex types were determined by PCR. The epidemiology of MRSA has been investigated by PFGE. Among 13 mecA positive strains, 12 were resistant to oxacillin (MIC = 3 to >256 μg/μl) and to cefoxitin and one strain was pre-resistant: susceptible to oxacillin (MIC = 0.19 μg/μl) and to cefoxitin. Hospital-acquired MRSA (HA-MRSA) strains had essentially SCCmec type IV (nine strains) or III (two strains) or I (one strain). One strain shown to carry ccrAB1 and ccrAB2 genes in combination with class B mec. Seven of 13 MRSA strains isolated from 2000 to 2006 were classified with major similarity group A harbored SCCmec type IV.