A better sequence-read simulator program for metagenomics

BACKGROUND: There are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors' original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data. RESULTS: We present BEAR (Better Emulation for Artificial Reads), a program that uses a machine-learning approach to generate reads with lengths and quality values that closely match empirically-derived distributions. BEAR can emulate reads from various sequencing platforms, including Illumina, 454, and Ion Torrent. BEAR requires minimal user input, as it automatically determines appropriate parameter settings from user-supplied data. BEAR also uses a unique method for deriving run-specific error rates, and extracts useful statistics from the metagenomic data itself, such as quality-error models. Many existing simulators are specific to a particular sequencing technology; however, BEAR is not restricted in this way. Because of its flexibility, BEAR is particularly useful for emulating the behaviour of technologies like Ion Torrent, for which no dedicated sequencing simulators are currently available. BEAR is also the first metagenomic sequencing simulator program that automates the process of generating abundances, which can be an arduous task. CONCLUSIONS: BEAR is useful for evaluating data processing tools in genomics. It has many advantages over existing comparable software, such as generating more realistic reads and being independent of sequencing technology, and has features particularly useful for metagenomics work

Adaptation of lactic acid bacteria for growth in beer

Growth of bacteria in beer leads to turbidity and off-flavors, resulting in a spoiled and unpalatable product and thus economic loss. The most common beer-spoilage organisms (BSOs) are lactic acid bacteria (LAB), with Lactobacillus and Pediococcus species being the most problematic. Because of the harsh environment (low nutrients, antimicrobial compounds ethanol and hops, anaerobic), only select isolates are able to sustain growth in and spoil beer. To begin understanding the phenomenon of LAB adapting to overcome stresses in beer, ethanol tolerance, hop resistance, and nutrient acquisition mechanisms were investigated. First, ethanol tolerance was analyzed in the context of beer-spoilage ability, and it was found that it is intrinsically high in LAB, thus leading to the conclusion that LAB ability to spoil beer is not dependent on ethanol resistance levels. This was then followed by genome sequencing of the BSO Pediococcus claussenii ATCC BAA-344T (Pc344) to elucidate mechanisms being used to resist hops and acquire low abundance or alternative nutrients. Subsequent analysis of Pc344 and Lactobacillus brevis BSO 464 via reverse transcription quantitative PCR demonstrated the variability found among BSOs in the presence of beer-spoilage-related genes and their use during growth in beer. Further analysis of Pc344 was performed via RNA-sequencing to get a global view of gene expression during mid-logarithmic growth in beer. It was found that several alternative nutrients were being used by Pc344 to sustain growth, and that hop resistance was enabled by a variety of mechanisms including oxidative stress response and pH control. Finally, genomic comparison of BSOs determined that conservation is only present for closely related organisms and that no specific genes/proteins are indicative of an isolate’s beer-spoilage potential. It is more likely that horizontal gene transfer plays a major role in LAB adaption for growth in beer, and that plasmids are very important for this evolution, as was demonstrated by plasmid-variants of Pc344. The main conclusions of this thesis are therefore that hop resistance is the main factor determining ability to grow in beer, and that transfer of genetic elements is the driving force behind LAB evolving into BSOs

Paleoenvironmental controls on the morphology and abundance of the coccolith Watznaueria Britannica (Late Jurassic, southern Germany)

International audienceThe coccolithophore species Watznaueria britannica is dominant in Middle-Upper Jurassic calcareous nannofossil assemblages and presents morphological variation, including different coccolith size, shape and length of the central area and of the bridge. Six morphotypes can be recognized in the polarizing light microscope. The aim of this work is to better understand the morphological variability of W. britannica and determine if this variability is controlled by paleoecological factors. In order to investigate the potential paleoecological controls on W. britannica morphology and abundance, we carried out a biometric study on a restricted temporal interval: the Late Oxfordian, in the Swabian Alb (southern Germany), characterized by increasing carbonate production linked to climatic changes. The Balingen–Tieringen section, where previous works on sedimentology, nannofossil assemblage composition, and δ18O and δ13C analyses were performed,was selected for this study. The variations in morphology and abundances ofW. britannicawere studied on 40 samples of the Balingen–Tieringen section, presenting variable lithologies and calcium carbonate contents. For each level, seven biometric parameters (coccolith length, width and ellipticity, central area length, width and ellipticity and central area proportion with respect to the coccolith) were measured or calculated on digitally captured images of the first 100 W. britannica coccoliths observed in the light microscope. The relationships between the different biometric variables were described using bivariate and Principal Component Analyses. Biometric parameters and Principal Component factors extracted from nannofossil assemblages as well as other paleoenvironmental proxies, were investigated using regression, and their stratigraphic trends were compared. Principal component analysis of the six biometric variables (3938 measurements) on W. britannica coccoliths shows a reduced morphological variability compared to a significant size gradient. An allometric trend recognized on the total placolith and on the central area within theW. britannica assemblages suggests that the different morphotypesmay represent intra-specific variability rather than different species.The general trend through Late Oxfordian shows an increase in size ofW. britannica coccoliths,mainly driven by an increase in the contribution of the largemorphotypes. Increasing placolith size is associated with drier and warmer climatic conditions during the latest Oxfordian

Transcriptome Sequence and Plasmid Copy Number Analysis of the Brewery Isolate <i>Pediococcus</i><i> claussenii</i> ATCC BAA-344^T during Growth in Beer

<div><p>Growth of specific lactic acid bacteria in beer leads to spoiled product and economic loss for the brewing industry. Microbial growth is typically inhibited by the combined stresses found in beer (e.g., ethanol, hops, low pH, minimal nutrients); however, certain bacteria have adapted to grow in this harsh environment. Considering little is known about the mechanisms used by bacteria to grow in and spoil beer, transcriptome sequencing was performed on a variant of the beer-spoilage organism <i>Pediococcus</i><i>claussenii</i> ATCC BAA-344^T (Pc344-358). Illumina sequencing was used to compare the transcript levels in Pc344-358 growing mid-exponentially in beer to those in nutrient-rich MRS broth. Various operons demonstrated high gene expression in beer, several of which are involved in nutrient acquisition and overcoming the inhibitory effects of hop compounds. As well, genes functioning in cell membrane modification and biosynthesis demonstrated significantly higher transcript levels in Pc344-358 growing in beer. Three plasmids had the majority of their genes showing increased transcript levels in beer, whereas the two cryptic plasmids showed slightly decreased gene expression. Follow-up analysis of plasmid copy number in both growth environments revealed similar trends, where more copies of the three non-cryptic plasmids were found in Pc344-358 growing in beer. Transcriptome sequencing also enabled the addition of several genes to the <i>P</i><i>. claussenii</i> ATCC BAA-344^T genome annotation, some of which are putatively transcribed as non-coding RNAs. The sequencing results not only provide the first transcriptome description of a beer-spoilage organism while growing in beer, but they also highlight several targets for future exploration, including genes that may have a role in the general stress response of lactic acid bacteria. </p> </div

Comparing gene expression results from RT-qPCR and RNA-seq.

<p>Differential expression of twenty genes was analyzed by RT-qPCR and calculated log₂ fold change in expression was plotted against the results obtained with transcriptome sequencing (statistical goodness of fit value is provided).</p

HPLC analysis of beer during Pc344-358 growth.

<p>Estimated concentrations of each compound were determined over time and plate counts were used to measure bacterial growth. Triplicate growth curves were analyzed, and standard deviations are indicated with error bars. Cellobiose and maltose could not be differentiated on the HPLC column, and are thus grouped together. Dextrin, ethanol, and glucose data are not included, as no change in concentration was found, or it was too low to detect (i.e., glucose).</p

GO term analysis.

<p>Enriched GO terms were found in groups of genes showing significantly higher transcript levels in beer (A) and in MRS-B (B). Each GO term is provided with its corresponding ontology category (BP = biological process; CC = cellular component; MF = molecular function). Only GO terms showing over-representation with a <i>p</i>-value < 0.05 (determined by GOseq) are depicted, with the size of each rectangle reflecting the associated <i>p</i>-value. Similar GO terms are visualized in the same color.</p