R. Gregory

A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling

BackgroundIn the last 5 years, the rapid pace of innovations and improvements in sequencing technologies has completely changed the landscape of metagenomic and metagenetic experiments. Therefore, it is critical to benchmark the various methodologies for interrogating the composition of microbial communities, so that we can assess their strengths and limitations. The most common phylogenetic marker for microbial community diversity studies is the 16S ribosomal RNA gene and in the last 10 years the field has moved from sequencing a small number of amplicons and samples to more complex studies where thousands of samples and multiple different gene regions are interrogated.ResultsWe assembled 2 synthetic communities with an even (EM) and uneven (UM) distribution of archaeal and bacterial strains and species, as metagenomic control material, to assess performance of different experimental strategies. The 2 synthetic communities were used in this study, to highlight the limitations and the advantages of the leading sequencing platforms: MiSeq (Illumina), The Pacific Biosciences RSII, 454 GS-FLX/+ (Roche), and IonTorrent (Life Technologies). We describe an extensive survey based on synthetic communities using 3 experimental designs (fusion primers, universal tailed tag, ligated adaptors) across the 9 hypervariable 16S rDNA regions. We demonstrate that library preparation methodology can affect data interpretation due to different error and chimera rates generated during the procedure. The observed community composition was always biased, to a degree that depended on the platform, sequenced region and primer choice. However, crucially, our analysis suggests that 16S rRNA sequencing is still quantitative, in that relative changes in abundance of taxa between samples can be recovered, despite these biases.ConclusionWe have assessed a range of experimental conditions across several next generation sequencing platforms using the most up-to-date configurations. We propose that the choice of sequencing platform and experimental design needs to be taken into consideration in the early stage of a project by running a small trial consisting of several hypervariable regions to quantify the discriminatory power of each region. We also suggest that the use of a synthetic community as a positive control would be beneficial to identify the potential biases and procedural drawbacks that may lead to data misinterpretation. The results of this study will serve as a guideline for making decisions on which experimental condition and sequencing platform to consider to achieve the best microbial profiling.

Adaptive Evolution of C4 Photosynthesis through Recurrent Lateral Gene Transfer

C(4) photosynthesis is a complex trait that confers higher productivity under warm and arid conditions. It has evolved more than 60 times via the co-option of genes present in C(3) ancestors followed by alteration of the patterns and levels of expression and adaptive changes in the coding sequences, but the evolutionary path to C(4) photosynthesis is still poorly understood. The grass lineage Alloteropsis offers unparalleled opportunities for studying C(4) evolution, because it includes a C(3) taxon and five C(4) species that vary significantly in C(4) anatomy and biochemistry. Using phylogenetic analyses of nuclear genes and leaf transcriptomes, we show that fundamental elements of the C(4) pathway in the grass lineage Alloteropsis were acquired via a minimum of four independent lateral gene transfers from C(4) taxa that diverged from this group more than 20 million years ago. The transfer of genes that were already fully adapted for C(4) function has occurred periodically over at least the last 10 million years and has been a recurrent source for the optimization of the C(4) pathway. This report shows that plant-plant lateral nuclear gene transfers can be a potent source of genetic novelty and adaptation in flowering plants.

A De Novo Expression Profiling of Anopheles funestus, Malaria Vector in Africa, Using 454 Pyrosequencing

Background Anopheles funestus is one of the major malaria vectors in Africa and yet there are few genomic tools available for this species compared to An. gambiae. To start to close this knowledge gap, we sequenced the An. funestus transcriptome using cDNA libraries developed from a pyrethroid resistant laboratory strain and a pyrethroid susceptible field strain from Mali. Results Using a pool of life stages (pupae, larvae, adults: females and males) for each strain, 454 sequencing generated 375,619 reads (average length of 182 bp). De novo assembly generated 18,103 contigs with average length of 253 bp. The average depth of coverage of these contigs was 8.3. In total 20.8% of all reads were novel when compared to reference databases. The sequencing of the field strain generated 204,758 reads compared to 170,861 from the insecticide resistant laboratory strain. The contigs most differentially represented in the resistant strain belong to the P450 gene family and cuticular genes which correlates with previous studies implicating both of these gene families in pyrethroid resistance. qPCR carried out on six contigs indicates that these ESTs could be suitable for gene expression studies such as microarray. 31,000 sites were estimated to contain Single Nucleotide Polymorphisms (SNPs) and analysis of SNPs from 20 contigs suggested that most of these SNPs are likely to be true SNPs. Gene conservation analysis confirmed the close phylogenetic relationship between An. funestus and An. gambiae. Conclusion This study represents a significant advance for the genetics and genomics of An. funestus since it provides an extensive set of both Expressed Sequence Tags (ESTs) and SNPs which can be readily adopted for the design of new genomic tools such as microarray or SNP platforms.

Parallel recruitment of multiple genes into c4 photosynthesis.

During the diversification of living organisms, novel adaptive traits usually evolve through the co-option of preexisting genes. However, most enzymes are encoded by gene families, whose members vary in their expression and catalytic properties. Each may therefore differ in its suitability for recruitment into a novel function. In this work, we test for the presence of such a gene recruitment bias using the example of C4 photosynthesis, a complex trait that evolved recurrently in flowering plants as a response to atmospheric CO2 depletion. We combined the analysis of complete nuclear genomes and high-throughput transcriptome data for three grass species that evolved the C4 trait independently. For five of the seven enzymes analyzed, the same gene lineage was recruited across the independent C4 origins, despite the existence of multiple copies. The analysis of a closely related C3 grass confirmed that C4 expression patterns were not present in the C3 ancestors but were acquired during the evolutionary transition to C4 photosynthesis. The significant bias in gene recruitment indicates that some genes are more suitable for a novel function, probably because the mutations they accumulated brought them closer to the characteristics required for the new function.

Evolvable social agents for bacterial systems modeling

We present two approaches to the individual-based modeling (IbM) of bacterial ecologies and evolution using computational tools. The IbM approach is introduced, and its important complementary role to biosystems modeling is discussed. A fine-grained model of bacterial evolution is then presented that is based on networks of interactivity between computational objects representing genes and proteins. This is followed by a coarser grained agent-based model, which is designed to explore the evolvability of adaptive behavioral strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of the two proposed individual-based bacterial models are discussed, and some results from simulation experiments are presented, illustrating their adaptive properties.

Individual-Based Modelling of Bacterial Ecologies and Evolution

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined.

Rule-based simulation of temperate bacteriophage infection: Restriction–modification as a limiter to infection in bacterial populations

An individual-based model (IbM) for bacterial adaptation and evolution, COSMIC-Rules, has been employed to simulate interactions of virtual temperate bacteriophages (phages) and their bacterial hosts. Outcomes of infection mimic those of a phage such as lambda, which can enter either the lytic or lysogenic cycle, depending on the nutritional status of the host. Infection of different hosts possessing differing restriction and modification systems is also simulated. Phages restricted upon infection of one restricting host can be adapted (by host-controlled modification of the phage genome) and subsequently propagate with full efficiency on this host. However, such ability is lost if the progeny phages are passaged through a new host with a different restriction and modification system before attempted re-infection of the original restrictive host. The simulations show that adaptation and re-adaptation to a particular host-controlled restriction and modification system result in lower efficiency and delayed lysis of bacterial cells compared with infection of non-restricting host bacteria. Such biologically realistic simulations validate the use of the IbM approach to predicting behaviour of bacteriophages in bacterial populations. The applicability of the model for more complex scenarios aimed at predictive modelling of bacterial evolution in a changing environment and the implications for the spread of viruses in a wider context are discussed.

Rule-based computing system for microbial interactions and communications : Evolution in virtual bacterial populations

We have developed a novel rule-based computing system of microbial interactions and communications, referred to as COSMIC-Rules, for simulating evolutionary processes within populations of virtual bacteria. The model incorporates three levels: the bacterial genome, the bacterial cell and an environment inhabited by such cells. The virtual environment in COSMIC-Rules can contain multiple substances, whose relative toxicity or nutrient status is specified by the genome of the bacterium. Each substance may be distributed uniformly or in a user-defined manner. The organisms in COSMIC-Rules possess individually-defined physical locations, size, cell division status and genomes. Genes and/or gene systems are represented by abstractions that may summate sometimes complex phenotypes. Central to COSMIC-Rules is a simplified representation of bacterial species, each containing a functional genome including, where desired, extrachromosomal elements such as plasmids and/or bacteriophages. A widely applicable computer representation of biological recognition systems based on bit string matching is essential to the model. This representation permits, for example, the modelling of protein-protein interactions, receptor-ligand interactions and DNA-DNA transactions. COSMIC-Rules is intended to inform studies on bacterial adaptation and evolution, and to predict behaviour of populations of pathogenic bacteria and their viruses. The framework is constructed for parallel execution across a large number of machines and efficiently utilises a 64 processor development cluster. It will run on any Grid system and has successfully tested simulations with millions of bacteria, of multiple species and utilising multiple substrates. The model may be used for large-scale simulations where a genealogical record for individual organisms is required.