High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants.

Our understanding of how microbes respond to micropollutants, such as pesticides, is almost wholly based on single-species responses to individual chemicals. However, in natural environments, microbes experience multiple pollutants simultaneously. Here we perform a matrix of multi-stressor experiments by assaying the growth of model and non-model strains of bacteria in all 255 combinations of 8 chemical stressors (antibiotics, herbicides, fungicides and pesticides). We found that bacterial strains responded in different ways to stressor mixtures, which could not be predicted simply from their phylogenetic relatedness. Increasingly complex chemical mixtures were both more likely to negatively impact bacterial growth in monoculture and more likely to reveal net interactive effects. A mixed co-culture of strains proved more resilient to increasingly complex mixtures and revealed fewer interactions in the growth response. These results show predictability in microbial population responses to chemical stressors and could increase the utility of next-generation eco-toxicological assays.

The temperature dependence of microbial community respiration is amplified by changes in species interactions.

Respiratory release of CO2 by microorganisms is one of the main components of the global carbon cycle. However, there are large uncertainties regarding the effects of climate warming on the respiration of microbial communities, owing to a lack of mechanistic, empirically tested theory that incorporates dynamic species interactions. We present a general mathematical model which predicts that thermal sensitivity of microbial community respiration increases as species interactions change from competition to facilitation (for example, commensalism, cooperation and mutualism). This is because facilitation disproportionately increases positive feedback between the thermal sensitivities of species-level metabolic and biomass accumulation rates at warmer temperatures. We experimentally validate our theoretical predictions in a community of eight bacterial taxa and show that a shift from competition to facilitation, after a month of co-adaptation, caused a 60% increase in the thermal sensitivity of respiration relative to de novo assembled communities that had not co-adapted. We propose that rapid changes in species interactions can substantially change the temperature dependence of microbial community respiration, which should be accounted for in future climate-carbon cycle models.

The role of competition versus cooperation in microbial community coalescence.

New microbial communities often arise through the mixing of two or more separately assembled parent communities, a phenomenon that has been termed "community coalescence". Understanding how the interaction structures of complex parent communities determine the outcomes of coalescence events is an important challenge. While recent work has begun to elucidate the role of competition in coalescence, that of cooperation, a key interaction type commonly seen in microbial communities, is still largely unknown. Here, using a general consumer-resource model, we study the combined effects of competitive and cooperative interactions on the outcomes of coalescence events. To do so, we simulate coalescence events between pairs of communities with different degrees of competition for shared carbon resources and cooperation through cross-feeding on leaked metabolic by-products (facilitation). We also study how structural and functional properties of post-coalescence communities evolve when they are subjected to repeated coalescence events. We find that in coalescence events, the less competitive and more cooperative parent communities contribute a higher proportion of species to the new community because of their superior ability to deplete resources and resist invasions. Consequently, when a community is subjected to repeated coalescence events, it gradually evolves towards being less competitive and more cooperative, as well as more speciose, robust and efficient in resource use. Encounters between microbial communities are becoming increasingly frequent as a result of anthropogenic environmental change, and there is great interest in how the coalescence of microbial communities affects environmental and human health. Our study provides new insights into the mechanisms behind microbial community coalescence, and a framework to predict outcomes based on the interaction structures of parent communities.

Refocusing multiple stressor research around the targets and scales of ecological impacts.

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

Systematic variation in the temperature dependence of bacterial carbon use efficiency.

Carbon use efficiency (CUE) is a key characteristic of microbial physiology and underlies community-level responses to changing environments. Yet, we currently lack general empirical insights into variation in microbial CUE at the level of individual taxa. Here, through experiments with 29 strains of environmentally isolated bacteria, we find that bacterial CUE typically responds either positively to temperature, or has no discernible response, within biologically meaningful temperature ranges. Using a global data synthesis, we show that these results are generalisable across most culturable groups of bacteria. This variation in the thermal responses of bacterial CUE is taxonomically structured, and stems from the fact that relative to respiration rates, bacterial population growth rates typically respond more strongly to temperature, and are also subject to weaker evolutionary constraints. Our results provide new insights into microbial physiology, and a basis for more accurately modelling the effects of thermal fluctuations on complex microbial communities.

The impact of intraspecific variation on food web structure.

Accounting for the variation that occurs within species in food webs can theoretically result in significant changes in both network structure and dynamics. However, there has been little work exploring their role with empirical data. In particular, the variation associated with species' life cycles, which is prevalent and represents both trait variation and taxonomic identity, has received little attention. Here, we characterize the structural consequences of life stage variation in five food webs, including a newly compiled web from the Arabian Gulf. We show that making life stage variation explicit in food webs results in larger food webs that possess consistent structural changes that are separate from the changes in structure that come simply from increasing the number of nodes in the webs. Furthermore, we show that the magnitude of these changes is related to ontogenetic specialism, the degree of overlap in the ecological niches of life stages. These results demonstrate the capacity of intraspecific variation to affect ecological networks and indicate the potential usefulness of stage-structured food webs, which capture size and taxonomic information, to represent variation below the species level.

The whole genome sequences and experimentally phased haplotypes of over 100 personal genomes.

BACKGROUND: Since the completion of the Human Genome Project in 2003, it is estimated that more than 200,000 individual whole human genomes have been sequenced. A stunning accomplishment in such a short period of time. However, most of these were sequenced without experimental haplotype data and are therefore missing an important aspect of genome biology. In addition, much of the genomic data is not available to the public and lacks phenotypic information. FINDINGS: As part of the Personal Genome Project, blood samples from 184 participants were collected and processed using Complete Genomics' Long Fragment Read technology. Here, we present the experimental whole genome haplotyping and sequencing of these samples to an average read coverage depth of 100X. This is approximately three-fold higher than the read coverage applied to most whole human genome assemblies and ensures the highest quality results. Currently, 114 genomes from this dataset are freely available in the GigaDB repository and are associated with rich phenotypic data; the remaining 70 should be added in the near future as they are approved through the PGP data release process. For reproducibility analyses, 20 genomes were sequenced at least twice using independent LFR barcoded libraries. Seven genomes were also sequenced using Complete Genomics' standard non-barcoded library process. In addition, we report 2.6 million high-quality, rare variants not previously identified in the Single Nucleotide Polymorphisms database or the 1000 Genomes Project Phase 3 data. CONCLUSIONS: These genomes represent a unique source of haplotype and phenotype data for the scientific community and should help to expand our understanding of human genome evolution and function.

Harvard Personal Genome Project: lessons from participatory public research.

BACKGROUND: Since its initiation in 2005, the Harvard Personal Genome Project has enrolled thousands of volunteers interested in publicly sharing their genome, health and trait data. Because these data are highly identifiable, we use an 'open consent' framework that purposefully excludes promises about privacy and requires participants to demonstrate comprehension prior to enrollment. DISCUSSION: Our model of non-anonymous, public genomes has led us to a highly participatory model of researcher-participant communication and interaction. The participants, who are highly committed volunteers, self-pursue and donate research-relevant datasets, and are actively engaged in conversations with both our staff and other Personal Genome Project participants. We have quantitatively assessed these communications and donations, and report our experiences with returning research-grade whole genome data to participants. We also observe some of the community growth and discussion that has occurred related to our project. SUMMARY: We find that public non-anonymous data is valuable and leads to a participatory research model, which we encourage others to consider. The implementation of this model is greatly facilitated by web-based tools and methods and participant education. Project results are long-term proactive participant involvement and the growth of a community that benefits both researchers and participants.

A public resource facilitating clinical use of genomes.

Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain-we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.

A survey of genomic traces reveals a common sequencing error, RNA editing, and DNA editing.

While it is widely held that an organism's genomic information should remain constant, several protein families are known to modify it. Members of the AID/APOBEC protein family can deaminate DNA. Similarly, members of the ADAR family can deaminate RNA. Characterizing the scope of these events is challenging. Here we use large genomic data sets, such as the two billion sequences in the NCBI Trace Archive, to look for clusters of mismatches of the same type, which are a hallmark of editing events caused by APOBEC3 and ADAR. We align 603,249,815 traces from the NCBI trace archive to their reference genomes. In clusters of mismatches of increasing size, at least one systematic sequencing error dominates the results (G-to-A). It is still present in mismatches with 99% accuracy and only vanishes in mismatches at 99.99% accuracy or higher. The error appears to have entered into about 1% of the HapMap, possibly affecting other users that rely on this resource. Further investigation, using stringent quality thresholds, uncovers thousands of mismatch clusters with no apparent defects in their chromatograms. These traces provide the first reported candidates of endogenous DNA editing in human, further elucidating RNA editing in human and mouse and also revealing, for the first time, extensive RNA editing in Xenopus tropicalis. We show that the NCBI Trace Archive provides a valuable resource for the investigation of the phenomena of DNA and RNA editing, as well as setting the stage for a comprehensive mapping of editing events in large-scale genomic datasets.

Free Factories: Unified Infrastructure for Data Intensive Web Services.

We introduce the Free Factory, a platform for deploying data-intensive web services using small clusters of commodity hardware and free software. Independently administered virtual machines called Freegols give application developers the flexibility of a general purpose web server, along with access to distributed batch processing, cache and storage services. Each cluster exploits idle RAM and disk space for cache, and reserves disks in each node for high bandwidth storage. The batch processing service uses a variation of the MapReduce model. Virtualization allows every CPU in the cluster to participate in batch jobs. Each 48-node cluster can achieve 4-8 gigabytes per second of disk I/O. Our intent is to use multiple clusters to process hundreds of simultaneous requests on multi-hundred terabyte data sets. Currently, our applications achieve 1 gigabyte per second of I/O with 123 disks by scheduling batch jobs on two clusters, one of which is located in a remote data center.