Jean-Pierre Flandrois

BIBI, a Bioinformatics Bacterial Identification Tool

ABSTRACT BIBI was designed to automate DNA sequence analysis for bacterial identification in the clinical field. BIBI relies on the use of BLAST and CLUSTAL W programs applied to different subsets of sequences extracted from GenBank. These sequences are filtered and stored in a new database, which is adapted to bacterial identification.

Use of PCR-Restriction Enzyme Pattern Analysis and Sequencing Database for hsp65 Gene-Based Identification of Nocardia Species

ABSTRACT Nocardia identification required laborious and time-consuming phenotypic and chemotaxonomic methods until molecular methods were developed in the mid-1990s. Here we reassessed the capacity of PCR-restriction enzyme pattern analysis (PRA) of the hsp65 gene to differentiate Nocardia species, including 36 new species. Our results confirm that hsp65 PRA must no longer be used for Nocardia species identification, as many species have the same restriction pattern. We then compared sequencing-based strategies using an hsp65 database and a 16S rRNA database and found that the hsp65 region contained sufficient polymorphisms for comprehensive Nocardia species identification.

Modelling the competitive growth of Listeria monocytogenes and Listeria innocua in enrichment broths

The overgrowth of Listeria innocua in enrichment broths designed for the isolation of Listeria monocytogenes is believed to result from two factors: a selective growth advantage of L. innocua, and/or an inhibitory interspecies interaction. The generation times of 13 isolates of L. innocua and L. monocytogenes were determined in Brain Heart Infusion (BHI) and a variety of enrichment media. No significant differences were found in growth characteristics between either species in the various media, suggesting that the growth advantage of L. innocua in enrichment media was not as significant as previously described. Kinetic analysis of mixed cultures of L. monocytogenes and isolates of L. innocua producing a variety of inhibitory activities demonstrated the possibility of an inhibitory interaction between these two species resulting in the overgrowth of the enrichment culture with L. innocua. Modelling the evolution of the ratio between two populations in an enrichment process was used to analyze the impact of a selective growth advantage in L. innocua in an enrichment process for growth of L. monocytogenes. These findings support the widely held view that an overgrowth of L. innocua in the enrichment process can result from both a selective growth advantage as well as the production of inhibitory compounds. From a practical perspective, these interactions can result in an increase in false negatives.

Accuracy of microbial growth predictions with square root and polynomial models.

The results of growth predictions using square root and polynomial models published in 14 papers were studied. Errors on quantities of practical interest such as lag time, generation time or the time required to reach a given increase in number of cells, are analyzed. The distribution of these errors was examined with the perspective of the practical use of predictive models in food industry. Highly unsafe predictions and significant average errors were observed in some cases. A good knowledge of predictive models accuracy seems essential for their efficient and safe use, for example to predict the shelf life of a product. Yet, authors generally gave no pragmatic information on such things as the average relative error or the range of errors on predicted variables. Problems of robustness of models when tested in different conditions were noticed, which corroborates the necessity of a systematic validation of models on new data.

A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy

Tumor angiogenesis is the process by which new blood vessels are formed and enhance the oxygenation and growth of tumors. As angiogenesis is recognized as being a critical event in cancer development, considerable efforts have been made to identify inhibitors of this process. Cytostatic treatments that target the molecular events of the angiogenesis process have been developed, and have met with some success. However, it is usually difficult to preclinically assess the effectiveness of targeted therapies, and apparently promising compounds sometimes fail in clinical trials. We have developed a multiscale mathematical model of angiogenesis and tumor growth. At the molecular level, the model focuses on molecular competition between pro- and anti-angiogenic substances modeled on the basis of pharmacological laws. At the tissue scale, the model uses partial differential equations to describe the spatio-temporal changes in cancer cells during three stages of the cell cycle, as well as those of the endothelial cells that constitute the blood vessel walls. This model is used to qualitatively assess how efficient endostatin gene therapy is. Endostatin is an anti-angiogenic endogenous substance. The gene therapy entails overexpressing endostatin in the tumor and in the surrounding tissue. Simulations show that there is a critical treatment dose below which increasing the duration of treatment leads to a loss of efficacy. This theoretical model may be useful to evaluate the efficacy of therapies targeting angiogenesis, and could therefore contribute to designing prospective clinical trials.

A seven-gene, multilocus, genus-wide approach to the phylogeny of mycobacteria using supertrees

This is the first study that estimates mycobacterial phylogeny using the maximum-likelihood method (PhyML-aLRT) on a seven-gene concatenate (hsp65, rpoB, 16S rRNA, smpB, sodA, tmRNA and tuf) and the super distance matrix (SDM) supertree method. Two sets of sequences were studied: a complete seven gene sequence set (set R, type strains of 87 species) and an incomplete set (set W, 132 species) with some missing data. Congruencies were computed by using the consense program (phylip package). The evolution rate of each gene was determined, as was the evolution rate of each strain for a given gene. Maximum-likelihood trees resulting from concatenation of the R and W sets resulted in a similar phylogeny, usually showing an early separation between slow-growing (SG) and rapidly growing (RG) mycobacteria. The SDM tree for the W set resulted in a different phylogeny. The separation of SG and RG was still evident, but it was located later in the nodes. The SG were therefore positioned as a subgroup of RG. Maximum-likelihood phylogenetic reconstruction was less affected by increasing the number of strains (with incomplete data), but did seem to cushion the variability of the evolution rate (ER), whereas the SDM method seemed to be more accurate and took into account both the differing ER values and the incomplete data. With regard to ER, it was observed that the 16S rRNA gene was the gene that displayed the slowest evolution, whereas smpB was the most rapidly evolving gene. Surprisingly, these two genes alone accurately separated the SG from the RG on the basis of their ER values. This study focused on the differences in ER between genes and in some cases linked the ER to the phenotypic classification of the mycobacteria.

What Is the Relevance of Obtaining Multiple Blood Samples for Culture? A Comprehensive Model to Optimize the Strategy for Diagnosing Bacteremia

Through a heuristic and probabilistic approach, we evaluated blood culture operating characteristics (sensitivity, specificity, and predictive values) as a function of several pretest parameters, together with their variability. On the basis of a meta-analysis of quantitative data from the literature, a model was developed and an estimation of the operating characteristics through numerical simulations (Monte Carlo method) was performed. The model evaluates the influence of ordering and drawing parameters on the ability of blood culture to distinguish bacteremic from nonbacteremic patients, regardless of the causative species. By considering the total blood volume to be cultured (six 5-10-mL bottles), results were found to confirm the current guidelines. On the basis of this hypothesis, the results, together with an analysis of the literature, failed to show any benefit of a strategy that involves obtaining multiple samples. The best strategy when performing blood culture is to obtain blood for 6 bottles (for a total volume of 35-42 mL), preferably at the same time.

Monod's bacterial growth model revisited

An attempt to justify Monods bacterial growth model is presented. The justification is based on a mechanistic approach to growth which leads to a differential equation with delay and then to Monods model. An unexpected increase of parameter K s with μ m is predicted by the theory. A survey of literature shows that this effect is present in a large majority of published data.

Early stages of in vitro killing curve of LY146032 and vancomycin for Staphylococcus aureus.

The early stages of the time-killing curves of vancomycin and LY146032 have been studied, by use of short sampling intervals, for three strains of Staphylococcus aureus. Both vancomycin and LY146032 killed S. aureus, but the time-killing curves differed: the effect of vancomycin was slow, limited, and not related to the concentration of the drug, whereas that of LY146032 was rapid, extensive, and related to concentration. When strains ATCC 25923 and CIP 6525 were exposed to LY146032, the population decreased exponentially with time. The killing rate was constant and linked to the concentration by a Michaelis-Menten relationship. The maximum killing rate and the affinity constant of LY146032, estimated from the data transformed by the Lineweaver-Burk method, differed for the two strains. The concentration of the antibiotic at which killing theoretically begins (estimated by linear regression using the logarithm of the concentration) is of the same magnitude as the MIC of LY146032, which indicates the pure bactericidal mode of action of the drug. S. aureus ATCC 12600 was more resistant to the bactericidal effect of the two drugs, and its killing curve did not conform to the model described here.

Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data.

ABSTRACT The following two factors significantly influence estimates of the maximum specific growth rate (μmax) and the lag-phase duration (λ): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of μmax and λ when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of λ.