Salmonella Prevalence in Raw Cocoa Beans and a Microbiological Risk Assessment to Evaluate the Impact of Cocoa Liquor Processing on the Reduction of Salmonella.

Salmonella in raw cocoa beans (n = 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3-46 MPN/g except for one sample (4.1 × 104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70-90% dark chocolate tablet) was set up to understand changes in Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting, or liquor sterilization (achieving a 0-6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose-response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35 × 10-8 (95% CI: 3.27 × 10-10-1.59 × 10-7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1-418). The estimated mean risk per serving was 3.35 × 10-9 (95% CI: 3.27 × 10-11-1.59 × 10-8), and the estimated salmonellosis cases per year was 9 (95% CI: <1-42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no postcontamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process was considered to be adequate.

PhenoLink--a web-tool for linking phenotype to ~omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains.

BACKGROUND: Linking phenotypes to high-throughput molecular biology information generated by ~omics technologies allows revealing cellular mechanisms underlying an organism's phenotype. ~Omics datasets are often very large and noisy with many features (e.g., genes, metabolite abundances). Thus, associating phenotypes to ~omics data requires an approach that is robust to noise and can handle large and diverse data sets. RESULTS: We developed a web-tool PhenoLink (http://bamics2.cmbi.ru.nl/websoftware/phenolink/) that links phenotype to ~omics data sets using well-established as well new techniques. PhenoLink imputes missing values and preprocesses input data (i) to decrease inherent noise in the data and (ii) to counterbalance pitfalls of the Random Forest algorithm, on which feature (e.g., gene) selection is based. Preprocessed data is used in feature (e.g., gene) selection to identify relations to phenotypes. We applied PhenoLink to identify gene-phenotype relations based on the presence/absence of 2847 genes in 42 Lactobacillus plantarum strains and phenotypic measurements of these strains in several experimental conditions, including growth on sugars and nitrogen-dioxide production. Genes were ranked based on their importance (predictive value) to correctly predict the phenotype of a given strain. In addition to known gene to phenotype relations we also found novel relations. CONCLUSIONS: PhenoLink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets. Visualization of links to phenotypes offered in PhenoLink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. PhenoLink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence (determined by e.g.: CGH or next-generation sequencing), gene expression (determined by e.g.: microarrays or RNA-seq), or metabolite abundance (determined by e.g.: GC-MS).

Phenotypic and genomic diversity of Lactobacillus plantarum strains isolated from various environmental niches.

Lactobacillus plantarum is a ubiquitous microorganism that is able to colonize several ecological niches, including vegetables, meat, dairy substrates and the gastro-intestinal tract. An extensive phenotypic and genomic diversity analysis was conducted to elucidate the molecular basis of the high flexibility and versatility of this species. First, 185 isolates from diverse environments were phenotypically characterized by evaluating their fermentation and growth characteristics. Strains clustered largely together within their particular food niche, but human fecal isolates were scattered throughout the food clusters, suggesting that they originate from the food eaten by the individuals. Based on distinct phenotypic profiles, 24 strains were selected and, together with a further 18 strains from an earlier low-resolution study, their genomic diversity was evaluated by comparative genome hybridization against the reference genome of L. plantarum WCFS1. Over 2000 genes were identified that constitute the core genome of the L. plantarum species, including 121 unique L. plantarum-marker genes that have not been found in other lactic acid bacteria. Over 50 genes unique for the reference strain WCFS1 were identified that were absent in the other L. plantarum strains. Strains of the L. plantarum subspecies argentoratensis were found to lack a common set of 24 genes, organized in seven gene clusters/operons, supporting their classification as a separate subspecies. The results provide a detailed view on phenotypic and genomic diversity of L. plantarum and lead to a better comprehension of niche adaptation and functionality of the organism.

Effect of soil sample preservation, compared to the effect of other environmental variables, on bacterial and eukaryotic diversity.

Archived soil samples are a valuable source for retrospective ecological studies, and their recent analysis using molecular ecological approaches has drawn significant attention within the scientific community. However, the possibility of addressing ecological questions regarding detectable microbiota in dried and extensively stored soils has not yet been fully evaluated. To achieve this, soil samples collected from two long-term grassland experiments in the United Kingdom and The Netherlands were subjected to air-drying at 40-42 degrees C and stored at room temperature. Total bacterial, Bacillus benzoevorans-related and eukaryotic communities associated with these samples were analyzed by DGGE-fingerprinting of PCR-amplified ribosomal RNA gene fragments. Changes in microbial community structure due to drying and storage were evaluated by multivariate analysis in relation to changes caused by other environmental conditions, such as soil pH, type of fertilizer and vegetation. Soil drying and storage affected the detectable community structure, but did not materially impair our capacity to identify the effect of soil parameters studied in long-term grassland experiments. Although, in some cases, the amplitude of the influence of a given parameter changed due to sample preservation, analyses revealed that pH, fertilization and soil type significantly influenced microbial community structure in the analyzed samples.

16S rRNA targeted DGGE fingerprinting of microbial communities.

The past decades have seen the staggering development of molecular microbial ecology as a discipline that uses the detection of so-called biomarkers to monitor microbial communities in environment samples. A variety of molecules can be used as biomarkers, including cell-wall components, proteins, lipids, DNA or RNA. Especially, the application of small subunit ribosomal RNA (rRNA) and the corresponding genes have proven invaluable for advances in microbial ecology. Several types of fingerprinting methods have been developed for the description of microbial communities in environmental samples. Among the most commonly used approaches is denaturing gradient gel electrophoresis (DGGE) of PCR-amplified fragments. DGGE allows separation of DNA fragment mixtures of equal length depending on their sequence. The separation is based on their sequence-specific melting point in a polyacrylamide gel with a gradient of a denaturant chemical (generally a combination of urea and formamide). DGGE allows for a rapid analysis and comparison of microbial communities. Compositional diversity can be visualized using DGGE where each band in principle represents a bacterial phylotype. After staining bands are visualized at each position in the gel where DNA molecules stopped migration. In principle, DGGE fingerprinting can resolve single base pair differences.

Eukaryotic diversity in historical soil samples.

The eukaryotic biodiversity in historical air-dried samples of Dutch agricultural soil has been assessed by random sequencing of an 18S rRNA gene library and by denaturing gradient gel electrophoresis. Representatives of nearly all taxa of eukaryotic soil microbes could be identified, demonstrating that it is possible to study eukaryotic microbiota in samples from soil archives that have been stored for more than 30 years at room temperature. In a pilot study, 41 sequences were retrieved that could be assigned to fungi and a variety of aerobic and anaerobic protists such as cercozoans, ciliates, xanthophytes (stramenopiles), heteroloboseans, and amoebozoans. A PCR-denaturing gradient gel electrophoresis analysis of samples collected between 1950 and 1975 revealed significant changes in the composition of the eukaryotic microbiota.

Occurrence of methanogenesis during start-up of a full-scale synthesis gas-fed reactor treating sulfate and metal-rich wastewater.

The start-up of a full-scale synthesis gas-fed gas-lift reactor treating metal and sulfate-rich wastewater was investigated. Sludge from a pilot-scale reactor was used to seed the full-scale reactor. The main difference in design between the pilot- and full-scale reactor was that metal precipitation and sulfate reduction occurred in the same reactor. After 7 weeks the full-scale reactor achieved the sulfate conversion design rate of 15 kg/m3day. Zinc sulfide precipitation inside the reactor did not interfere with obtaining a high rate of sulfate reduction. 16S rRNA gene analysis demonstrated that the bacterial communities in both reactors were dominated by the sulfate-reducing genus Desulfomicrobium. Archaeal communities of both reactors were dominated by the methanogenic genus Methanobacterium. Most Probable Number (MPN) counts confirmed that heterotrophic Sulfate-Reducing Bacteria (SRB) were dominant (10(11) -10(12) cells/g VSS) compared to homoacetogens (10(5) -10(6) cells/g VSS) and methanogens (10(8) -10(9) cells/g VSS). Methanogenesis was not suppressed during start-up of the full scale-reactor, despite the predominance of SRB, which have a lower hydrogen threshold. Due to the short sludge retention time (4-7 days) competition for hydrogen is determined by Monod kinetics, not hydrogen thresholds. As the kinetic parameters for SRB and methanogens are similar, methanogenesis may persist which results in a loss of hydrogen.

Development and application of a selective PCR-denaturing gradient gel electrophoresis approach to detect a recently cultivated Bacillus group predominant in soil.

The worldwide presence of a hitherto-nondescribed group of predominant soil microorganisms related to Bacillus benzoevorans was analyzed after development of two sets of selective primers targeting 16S rRNA genes in combination with denaturing gradient gel electrophoresis (DGGE). The high abundance and cultivability of at least some of these microorganisms makes them an appropriate subject for studies on their biogeographical dissemination and diversity. Since cultivability can vary significantly with the physiological state and even between closely related strains, we developed a culture-independent 16S rRNA gene-targeted DGGE fingerprinting protocol for the detection of these bacteria from soil samples. The composition of the B. benzoevorans relatives in the soil samples from The Netherlands, Bulgaria, Russia, Pakistan, and Portugal showed remarkable differences between the different countries. Differences in the DGGE profiles of these communities in archived soil samples from the Dutch Wieringermeer polder were observed over time during which a shift from anaerobic to aerobic and from saline to freshwater conditions occurred. To complement the molecular methods, we additionally cultivated B. benzoevorans-related strains from all of the soil samples. The highest number of B. benzoevorans relatives was found in the soils from the northern part of The Netherlands. The present study contributes to our knowledge of the diversity and abundance of this interesting group of microbes in soils throughout the world.