Pieter Busschaert

Bacterial community dynamics during industrial malting, with an emphasis on lactic acid bacteria.

Characterization of the microflora during malting is an essential step towards process management and optimization. Up till now, however, microbial characterization in the malting process has mostly been done using culture-dependent methods, probably leading to biased estimates of microbial diversity. The aim of this study was to characterize the bacterial communities using two culture-independent methods, including Terminal Restriction Fragment Length Polymorphism (T-RFLP) and 454 pyrosequencing, targeting the 16S rRNA gene. Studied samples originated from two harvest years and two malting houses malting the same batch of barley. Besides targeting the entire bacterial community (T-RFLP), emphasis was put on lactic acid bacteria (LAB) (T-RFLP and 454 pyrosequencing). The overall bacterial community richness was limited, but the community structure changed during the process. Zooming in on the LAB community using 454 pyrosequencing revealed a total of 47 species-level operational taxonomic units (OTUs). LAB diversity appeared relatively limited since 88% of the sequences were covered by the same five OTUs (representing members of Weissella, Lactobacillus and Leuconostoc) present in all samples investigated. Fluctuations in the relative abundances of the dominant LAB were observed with the process conditions. In addition, both the year of harvest and malting house influenced the LAB community structure.

Impact of metal pollution on fungal diversity and community structures

The impact of metal pollution on plant communities has been studied extensively in the past, but little is known about the effects of metal pollution on fungal communities that occur in metal-polluted soils. Metal-tolerant ecotypes of the ectomycorrhizal fungus Suillus luteus are frequently found in pioneer pine forests in the Campine region in Belgium on metal-polluted soils. We hypothesized that metal pollution would play an important role in shaping below-ground fungal communities that occur in these soils and that Suillus luteus would be a dominant player. To test these hypotheses, the fungal communities in a young pine plantation in soil polluted with zinc, and cadmium were studied using 454 amplicon pyrosequencing. Results show that zinc, cadmium and soil organic matter content were strongly correlated with the fungal community composition, but no effects on fungal diversity were observed. As hypothesized, S. luteus was found to be a dominant member of the studied fungal communities. However, other dominant fungal species, such as Sistotrema sp., Wilcoxina mikolae and Cadophora finlandica were found as well. Their presence in metal-polluted sites is discussed.

Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and uncertainty separated.

The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo-randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used-that is, an ANOVA-like model and Sobol sensitivity indices-to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats.

Diversity of dechlorination pathways and organohalide respiring bacteria in chlorobenzene dechlorinating enrichment cultures originating from river sludge

Anaerobic reductive dechlorination of hexachlorobenzene (HCB) and three isomers of tetrachlorobenzene (TeCB) (1,2,3,4-, 1,2,3,5- and 1,2,4,5-TeCB) was investigated in microcosms containing chloroaromatic contaminated river sediment. All chlorobenzenes were dechlorinated to dichlorobenzene (DCB) or monochlorobenzene. From the sediment, a methanogenic sediment-free culture was obtained which dechlorinated HCB, pentachlorobenzene, three TeCB isomers, three trichlorobenzene (TCB) isomers (1,2,3-, 1,2,4- and 1,3,5-TCB) and 1,2-DCB. Dechlorination involved multiple pathways including the removal of doubly flanked, singly flanked and isolated chlorine substituents. 454-pyrosequencing of partial bacterial 16S rRNA genes amplified from selected chlorobenzene dechlorinating sediment-free enrichment cultures revealed the presence of a variety of bacterial species, including Dehalobacter and Dehalococcoides mccartyi, that were previously documented as organohalide respiring bacteria. A genus with apparent close relationship to Desulfitobacterium that also has been associated with organohalide respiration, composed the major fraction of the operational taxonomic units (OTUs). Another major OTU was linked with Sedimentibacter sp., a genus that was previously identified in strict co-cultures of consortia reductively dehalogenating chlorinated compounds. Our data point towards the existence of multiple interactions within highly chlorinated benzene dechlorinating communities.

An educationally inspired illustration of two-dimensional Quantitative Microbiological Risk Assessment (QMRA) and sensitivity analysis.

Quantitative Microbiological Risk Assessment (QMRA) is a structured methodology used to assess the risk involved by ingestion of a pathogen. It applies mathematical models combined with an accurate exploitation of data sets, represented by distributions and - in the case of two-dimensional Monte Carlo simulations - their hyperparameters. This research aims to highlight background information, assumptions and truncations of a two-dimensional QMRA and advanced sensitivity analysis. We believe that such a detailed listing is not always clearly presented in actual risk assessment studies, while it is essential to ensure reliable and realistic simulations and interpretations. As a case-study, we are considering the occurrence of listeriosis in smoked fish products in Belgium during the period 2008-2009, using two-dimensional Monte Carlo and two sensitivity analysis methods (Spearman correlation and Sobol sensitivity indices) to estimate the most relevant factors of the final risk estimate. A risk estimate of 0.018% per consumption of contaminated smoked fish by an immunocompromised person was obtained. The final estimate of listeriosis cases (23) is within the actual reported result obtained for the same period and for the same population. Variability on the final risk estimate is determined by the variability regarding (i) consumer refrigerator temperatures, (ii) the reference growth rate of L. monocytogenes, (iii) the minimum growth temperature of L. monocytogenes and (iv) consumer portion size. Variability regarding the initial contamination level of L. monocytogenes tends to appear as a determinant of risk variability only when the minimum growth temperature is not included in the sensitivity analysis; when it is included the impact regarding the variability on the initial contamination level of L. monocytogenes is disappearing. Uncertainty determinants of the final risk indicated the need of gathering more information on the reference growth rate and the minimum growth temperature of L. monocytogenes. Uncertainty in the dose-response relationship was not included in the analysis, hence the level of its influence cannot be assessed in the present research. Finally, a baseline global workflow for QMRA and sensitivity analysis is proposed.