Background

Cheese contamination can occur at numerous stages in the manufacturing process including the use of improperly pasteurized or raw milk. Of concern is the potential contamination by Listeria monocytogenes and other pathogenic bacteria that find the high moisture levels and moderate pH of popular Latin-style cheeses like queso fresco a hospitable environment. In the investigation of a foodborne outbreak, samples typically undergo enrichment in broth for 24 hours followed by selective agar plating to isolate bacterial colonies for confirmatory testing. The broth enrichment step may also enable background microflora to proliferate, which can confound subsequent analysis if not inhibited by effective broth or agar additives. We used 16S rRNA gene sequencing to provide a preliminary survey of bacterial species associated with three brands of Latin-style cheeses after 24-hour broth enrichment.

Results

Brand A showed a greater diversity than the other two cheese brands (Brands B and C) at nearly every taxonomic level except phylum. Brand B showed the least diversity and was dominated by a single bacterial taxon, Exiguobacterium, not previously reported in cheese. This genus was also found in Brand C, although Lactococcus was prominent, an expected finding since this bacteria belongs to the group of lactic acid bacteria (LAB) commonly found in fermented foods.

Conclusions

The contrasting diversity observed in Latin-style cheese was surprising, demonstrating that despite similarity of cheese type, raw materials and cheese making conditions appear to play a critical role in the microflora composition of the final product. The high bacterial diversity associated with Brand A suggests it may have been prepared with raw materials of high bacterial diversity or influenced by the ecology of the processing environment. Additionally, the presence of Exiguobacterium in high proportions (96%) in Brand B and, to a lesser extent, Brand C (46%), may have been influenced by the enrichment process. This study is the first to define Latin-style cheese microflora using Next-Generation Sequencing. These valuable preliminary data will direct selective tailoring of agar formulations to improve culture-based detection of pathogens in Latin-style cheese.

Background

Enriching environmental samples to increase the probability of detection has been standard practice throughout the history of microbiology. However, by its very nature, the process of enrichment creates a biased sample that may have unintended consequences for surveillance or resolving a pathogenic outbreak. With the advent of next-generation sequencing and metagenomic approaches, the possibility now exists to quantify enrichment bias at an unprecedented taxonomic breadth.

Findings

We investigated differences in taxonomic profiles of three enriched and unenriched tomato phyllosphere samples taken from three different tomato fields (n = 18). 16S rRNA gene meteganomes were created for each of the 18 samples using 454/Roche’s pyrosequencing platform, resulting in a total of 165,259 sequences. Significantly different taxonomic profiles and abundances at a number of taxonomic levels were observed between the two treatments. Although as many as 28 putative Salmonella sequences were detected in enriched samples, there was no significant difference in the abundance of Salmonella between enriched and unenriched treatments.

Conclusions

Our results illustrate that the process of enriching greatly alters the taxonomic profile of an environmental sample beyond that of the target organism. We also found evidence suggesting that enrichment may not increase the probability of detecting a target. In conclusion, our results further emphasize the need to develop metagenomics as a validated culture independent method for pathogen detection.

Background

Marine sponge species are of significant interest to many scientific fields including marine ecology, conservation biology, genetics, host-microbe symbiosis and pharmacology. One of the most intriguing aspects of the sponge “holobiont” system is the unique physiology, interaction with microbes from the marine environment and the development of a complex commensal microbial community. However, intraspecific variability and temporal stability of sponge-associated bacterial symbionts remain relatively unknown.

Methodology/Principal Findings

We have characterized the bacterial symbiont community biodiversity of seven different individuals of the Caribbean reef sponge Axinella corrugata, from two different Florida reef locations during variable seasons using multiplex 454 pyrosequencing of 16 S rRNA amplicons. Over 265,512 high-quality 16 S rRNA sequences were generated and analyzed. Utilizing versatile bioinformatics methods and analytical software such as the QIIME and CloVR packages, we have identified 9,444 distinct bacterial operational taxonomic units (OTUs). Approximately 65,550 rRNA sequences (24%) could not be matched to bacteria at the class level, and may therefore represent novel taxa. Differentially abundant classes between seasonal Axinella communities included Gammaproteobacteria, Flavobacteria, Alphaproteobacteria, Cyanobacteria, Acidobacter and Nitrospira. Comparisons with a proximal outgroup sponge species (Amphimedon compressa), and the growing sponge symbiont literature, indicate that this study has identified approximately 330 A. corrugata-specific symbiotic OTUs, many of which are related to the sulfur-oxidizing Ectothiorhodospiraceae. This family appeared exclusively within A. corrugata, comprising >34.5% of all sequenced amplicons. Other A. corrugata symbionts such as Deltaproteobacteria, Bdellovibrio, and Thiocystis among many others are described.

Conclusions/Significance

Slight shifts in several bacterial taxa were observed between communities sampled during spring and fall seasons. New 16 S rDNA sequences and concomitant identifications greatly expand the microbial community profile for this model reef sponge, and will likely be useful as a baseline for any future comparisons regarding sponge microbial community dynamics.

Celiac disease (CD) is a unique autoimmune disorder in which the genetic factors (DQ2/DQ8) and the environmental trigger (gluten) are known and necessary but not sufficient for its development. Other environmental components contributing to CD are poorly understood. Studies suggest that aspects of gluten intake might influence the risk of CD occurrence and timing of its onset, i.e., the amount and quality of ingested gluten, together with the pattern of infant feeding and the age at which gluten is introduced in the diet. In this study, we hypothesize that the intestinal microbiota as a whole rather than specific infections dictates the switch from tolerance to immune response in genetically susceptible individuals. Using a sample of infants genetically at risk of CD, we characterized the longitudinal changes in the microbial communities that colonize infants from birth to 24 months and the impact of two patterns of gluten introduction (early vs. late) on the gut microbiota and metabolome, and the switch from gluten tolerance to immune response, including onset of CD autoimmunity. We show that infants genetically susceptible to CD who are exposed to gluten early mount an immune response against gluten and develop CD autoimmunity more frequently than at-risk infants in which gluten exposure is delayed until 12 months of age. The data, while derived from a relatively small number of subjects, suggest differences between the developing microbiota of infants with genetic predisposition for CD and the microbiota from infants with a non-selected genetic background, with an overall lack of bacteria of the phylum Bacteriodetes along with a high abundance of Firmicutes and microbiota that do not resemble that of adults even at 2 years of age. Furthermore, metabolomics analysis reveals potential biomarkers for the prediction of CD. This study constitutes a definite proof-of-principle that these combined genomic and metabolomic approaches will be key to deciphering the role of the gut microbiota on CD onset.

Background

The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly.

Results

We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers.

Conclusions

Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Background

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software.

Results

We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms.

Conclusion

The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

Background

Tomato (Solanum lycopersicum) consumption has been one of the most common causes of produce-associated salmonellosis in the United States. Contamination may originate from animal waste, insects, soil or water. Current guidelines for fresh tomato production recommend the use of potable water for applications coming in direct contact with the fruit, but due to high demand, water from other sources is frequently used. We sought to describe the overall bacterial diversity on the surface of tomato fruit and the effect of two different water sources (ground and surface water) when used for direct crop applications by generating a 454-pyrosequencing 16S rRNA dataset of these different environments. This study represents the first in depth characterization of bacterial communities in the tomato fruit surface and the water sources commonly used in commercial vegetable production.

Results

The two water sources tested had a significantly different bacterial composition. Proteobacteria was predominant in groundwater samples, whereas in the significantly more diverse surface water, abundant phyla also included Firmicutes, Actinobacteria and Verrucomicrobia. The fruit surface bacterial communities on tomatoes sprayed with both water sources could not be differentiated using various statistical methods. Both fruit surface environments had a high representation of Gammaproteobacteria, and within this class the genera Pantoea and Enterobacter were the most abundant.

Conclusions

Despite the major differences observed in the bacterial composition of ground and surface water, the season long use of these very different water sources did not have a significant impact on the bacterial composition of the tomato fruit surface. This study has provided the first next-generation sequencing database describing the bacterial communities living in the fruit surface of a tomato crop under two different spray water regimes, and therefore represents an important step forward towards the development of science-based metrics for Good Agricultural Practices.

Background

Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, there exists significant variability in analysis methods employed in these studies.

Results

Here we provide a critical assessment of current analysis methodologies that cluster sequences into operational taxonomic units (OTUs) and demonstrate that small changes in algorithm parameters can lead to significantly varying results. Our analysis provides strong evidence that the species-level diversity estimates produced using common OTU methodologies are inflated due to overly stringent parameter choices. We further describe an example of how semi-supervised clustering can produce OTUs that are more robust to changes in algorithm parameters.

Conclusions

Our results highlight the need for systematic and open evaluation of data analysis methodologies, especially as targeted 16S rRNA diversity studies are increasingly relying on high-throughput sequencing technologies. All data and results from our study are available through the JGI FAMeS website http://fames.jgi-psf.org/.

The extent of chromosome rearrangements in Pyrococcus isolates from marine hydrothermal vents in Vulcano Island, Italy, was evaluated by high-throughput genomic methods. The results illustrate the dynamic nature of the genomes of the genus Pyrococcus and raise the possibility of a connection between rapidly changing environmental conditions and adaptive genomic properties.

The assembly methods used for whole-genome shotgun (WGS) data have a major impact on the quality of resulting draft genomes. We present a novel algorithm to generate a set of “reliable” overlaps based on identifying repeat k-mers. To demonstrate the benefits of using reliable overlaps, we have created a version of the Phrap assembly program that uses only overlaps from a specific list. We call this version PhrapUMD. Integrating PhrapUMD and our “reliable-overlap” algorithm with the Baylor College of Medicine assembler, Atlas, we assemble the BACs from the Rattus norvegicus genome project. Starting with the same data as the Nov. 2002 Atlas assembly, we compare our results and the Atlas assembly to the 4.3 Mb of rat sequence in the 21 BACs that have been finished. Our version of the draft assembly of the 21 BACs increases the coverage of finished sequence from 93.4% to 96.3%, while simultaneously reducing the base error rate from 4.5 to 1.1 errors per 10,000 bases. There are a number of ways of assessing the relative merits of assemblies when the finished sequence is available. If one views the overall quality of an assembly as proportional to the inverse of the product of the error rate and sequence missed, then the assembly presented here is seven times better. The UMD Overlapper with options for reliable overlaps is available from the authors at http://www.genome.umd.edu. We also provide the changes to the Phrap source code enabling it to use only the reliable overlaps.