Zucchini-dependent piRNA processing is triggered by recruitment to the cytoplasmic processing machinery.

The piRNA pathway represses transposable elements in the gonads and thereby plays a vital role in protecting the integrity of germline genomes of animals. Mature piRNAs are processed from longer transcripts, piRNA precursors (pre-piRNAs). In Drosophila, processing of pre-piRNAs is initiated by piRNA-guided Slicer cleavage or the endonuclease Zucchini (Zuc). As Zuc does not have any sequence or structure preferences in vitro, it is not known how piRNA precursors are selected and channeled into the Zuc-dependent processing pathway. We show that a heterologous RNA that lacks complementary piRNAs is processed into piRNAs upon recruitment of several piRNA pathway factors. This processing requires Zuc and the helicase Armitage (Armi). Aubergine (Aub), Argonaute 3 (Ago3), and components of the nuclear RDC complex, which are required for normal piRNA biogenesis in germ cells, are dispensable. Our approach allows discrimination of proteins involved in the transcription and export of piRNA precursors from components required for the cytoplasmic processing steps. piRNA processing correlates with localization of the substrate RNA to nuage, a distinct membraneless cytoplasmic compartment, which surrounds the nucleus of germ cells, suggesting that sequestration of RNA to this subcellular compartment is both necessary and sufficient for selecting piRNA biogenesis substrates.

Functional properties of cream and butter oil from milk of Holstein cows abomasally infused with increasing amounts of high-oleic sunflower fatty acids.

This research paper addresses the hypothesis that there is an optimal amount of intestinally available oleic acid (provided via abomasal infusion) to produce higher-oleic acid milk fat with satisfactory functional characteristics of cream and butter oil. A control and four increasing doses of free fatty acids from high oleic sunflower oil (HOSFA) were infused into the abomasum of four lactating dairy cows in a crossover experimental design with 7-d periods. Treatments were: (1) control (no HOSFA infused), (2) HOSFA (250 g/d), (3) HOSFA (500 g/d), (4) HOSFA (750 g/d), and (5) HOSFA (1000 g/d). All treatments included meat solubles and Tween 80 as emulsifiers. Viscosity, overrun and whipping time as well as foam firmness and stability were evaluated in whipping creams (33% fat). Solid fat content (from 0 to 40°C), melting point and firmness were determined in butter oil. Whipping time of cream increased linearly and viscosity decreased linearly as infusion of HOSFA increased. Overrun displayed a quadratic response, decreasing when 500 g/d or more was infused. Foam firmness and stability were not affected significantly by HOSFA. For butter oil, melting point, firmness, and solid fat content decreased as HOSFA infusion increased. Changes in 21 TG fractions were statistically correlated to functional properties, with 6-10 fractions showing the highest correlations consistently. Decisions on the optimal amount of HOSFA were dependent on the dairy product to which milk fat is applied. For products handled at commercial refrigeration temperatures, such as whipping cream and butter oil, the 250 g/d level was the limit to maintain satisfactory functional qualities. Palmitic acid needed to be present in at least 20% in milk fat to keep the functional properties for the products.

Aub and Ago3 Are Recruited to Nuage through Two Mechanisms to Form a Ping-Pong Complex Assembled by Krimper.

In Drosophila, two Piwi proteins, Aubergine (Aub) and Argonaute-3 (Ago3), localize to perinuclear "nuage" granules and use guide piRNAs to target and destroy transposable element transcripts. We find that Aub and Ago3 are recruited to nuage by two different mechanisms. Aub requires a piRNA guide for nuage recruitment, indicating that its localization depends on recognition of RNA targets. Ago3 is recruited to nuage independently of a piRNA cargo and relies on interaction with Krimper, a stable component of nuage that is able to aggregate in the absence of other nuage proteins. We show that Krimper interacts directly with Aub and Ago3 to coordinate the assembly of the ping-pong piRNA processing (4P) complex. Symmetrical dimethylated arginines are required for Aub to interact with Krimper, but they are dispensable for Ago3 to bind Krimper. Our study reveals a multi-step process responsible for the assembly and function of nuage complexes in piRNA-guided transposon repression.

Gene Expression and Pathway Analysis in Rat Brain and Liver After Exposure to Royal Demolition Explosive (Hexahydro-1,3,5-Trinitro-1,3,5-Triazine).

The nitramine explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is associated with acute and chronic toxicity in mammals and targets both the central nervous system and liver. After a single oral dose of RDX in male rats, the systemic distribution of RDX and the toxicodynamic response was measured using clinical chemistry and Affymetrix Rat Genome® 230 2.0 gene expression arrays, respectively. Nominal doses of 0, 9 and 36 mg/kg pure RDX were administered to animals followed by liver, cerebral cortex, and hippocampus gene expression analysis at 0, 3.5, 24, and 48 hours. RDX quickly entered the liver and brain, increasing up to 24 hours. For the 36 mg/kg dose, RDX was still measurable in liver and brain at 48 hours, but was non-detectible for the 9 mg/kg dose. At 3.5 hours, the time within which most convulsions reportedly occur after RDX ingestion, the hippocampus displayed the highest response for both gene expression and pathways, while the cortex was relatively non-responsive. The top 2 impacted pathways, primarily involved in neurotransmission, were the GABAergic and glutamatergic pathways. High numbers of genes also responded to RDX in the liver with P450 metabolism pathways significantly involved. Compared to the liver, the hippocampus displayed more consistent biological effects across dose and time with neurotransmission pathways predominating. Overall, based on gene expression data, RDX responses were high in both the hippocampus and liver, but were minimal in the cerebral cortex. These results identify the hippocampus as an important target for RDX based on gene expression.

Current ecotoxicity testing needs among selected U.S. federal agencies.

U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies' regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations.

Building biosecurity for synthetic biology.

The fast-paced field of synthetic biology is fundamentally changing the global biosecurity framework. Current biosecurity regulations and strategies are based on previous governance paradigms for pathogen-oriented security, recombinant DNA research, and broader concerns related to genetically modified organisms (GMOs). Many scholarly discussions and biosecurity practitioners are therefore concerned that synthetic biology outpaces established biosafety and biosecurity measures to prevent deliberate and malicious or inadvertent and accidental misuse of synthetic biology's processes or products. This commentary proposes three strategies to improve biosecurity: Security must be treated as an investment in the future applicability of the technology; social scientists and policy makers should be engaged early in technology development and forecasting; and coordination among global stakeholders is necessary to ensure acceptable levels of risk.

Piwi induces piRNA-guided transcriptional silencing and establishment of a repressive chromatin state.

In the metazoan germline, piwi proteins and associated piwi-interacting RNAs (piRNAs) provide a defense system against the expression of transposable elements. In the cytoplasm, piRNA sequences guide piwi complexes to destroy complementary transposon transcripts by endonucleolytic cleavage. However, some piwi family members are nuclear, raising the possibility of alternative pathways for piRNA-mediated regulation of gene expression. We found that Drosophila Piwi is recruited to chromatin, colocalizing with RNA polymerase II (Pol II) on polytene chromosomes. Knockdown of Piwi in the germline increases expression of transposable elements that are targeted by piRNAs, whereas protein-coding genes remain largely unaffected. Derepression of transposons upon Piwi depletion correlates with increased occupancy of Pol II on their promoters. Expression of piRNAs that target a reporter construct results in a decrease in Pol II occupancy and an increase in repressive H3K9me3 marks and heterochromatin protein 1 (HP1) on the reporter locus. Our results indicate that Piwi identifies targets complementary to the associated piRNA and induces transcriptional repression by establishing a repressive chromatin state when correct targets are found.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Keanu: a novel visualization tool to explore biodiversity in metagenomes.

BACKGROUND: One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier. RESULTS: Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska. CONCLUSIONS: Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments. Keanu is written in Python and is freely available at https://github.com/IGBB/keanu .

Co-evolution of physical and social sciences in synthetic biology.

Emerging technologies research often covers various perspectives in disciplines and research areas ranging from hard sciences, engineering, policymaking, and sociology. However, the interrelationship between these different disciplinary domains, particularly the physical and social sciences, often occurs many years after a technology has matured and moved towards commercialization. Synthetic biology may serve an exception to this idea, where, since 2000, the physical and the social sciences communities have increasingly framed their research in response to various perspectives in biological engineering, risk assessment needs, governance challenges, and the social implications that the technology may incur. This paper reviews a broad collection of synthetic biology literature from 2000-2016, and demonstrates how the co-development of physical and social science communities has grown throughout synthetic biology's earliest stages of development. Further, this paper indicates that future co-development of synthetic biology scholarship will assist with significant challenges of the technology's risk assessment, governance, and public engagement needs, where an interdisciplinary approach is necessary to foster sustainable, risk-informed, and societally beneficial technological advances moving forward.

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.

Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor ß (TGF-ß) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an adverse outcome pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted a systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing a potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in the ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.

Multiple environmental stressors induce complex transcriptomic responses indicative of phenotypic outcomes in Western fence lizard.

BACKGROUND: The health and resilience of species in natural environments is increasingly challenged by complex anthropogenic stressor combinations including climate change, habitat encroachment, and chemical contamination. To better understand impacts of these stressors we examined the individual- and combined-stressor impacts of malaria infection, food limitation, and 2,4,6-trinitrotoluene (TNT) exposures on gene expression in livers of Western fence lizards (WFL, Sceloporus occidentalis) using custom WFL transcriptome-based microarrays. RESULTS: Computational analysis including annotation enrichment and correlation analysis identified putative functional mechanisms linking transcript expression and toxicological phenotypes. TNT exposure increased transcript expression for genes involved in erythropoiesis, potentially in response to TNT-induced anemia and/or methemoglobinemia and caused dose-specific effects on genes involved in lipid and overall energy metabolism consistent with a hormesis response of growth stimulation at low doses and adverse decreases in lizard growth at high doses. Functional enrichment results were indicative of inhibited potential for lipid mobilization and catabolism in TNT exposures which corresponded with increased inguinal fat weights and was suggestive of a decreased overall energy budget. Malaria infection elicited enriched expression of multiple immune-related functions likely corresponding to increased white blood cell (WBC) counts. Food limitation alone enriched functions related to cellular energy production and decreased expression of immune responses consistent with a decrease in WBC levels. CONCLUSIONS: Despite these findings, the lizards demonstrated immune resilience to malaria infection under food limitation with transcriptional results indicating a fully competent immune response to malaria, even under bio-energetic constraints. Interestingly, both TNT and malaria individually increased transcriptional expression of immune-related genes and increased overall WBC concentrations in blood; responses that were retained in the TNT x malaria combined exposure. The results demonstrate complex and sometimes unexpected responses to multiple stressors where the lizards displayed remarkable resiliency to the stressor combinations investigated.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology.

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17ß-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.

Prioritization of Contaminants of Emerging Concern in Wastewater Treatment Plant Discharges Using Chemical:Gene Interactions in Caged Fish.

We examined whether contaminants present in surface waters could be prioritized for further assessment by linking the presence of specific chemicals to gene expression changes in exposed fish. Fathead minnows were deployed in cages for 2, 4, or 8 days at three locations near two different wastewater treatment plant discharge sites in the Saint Louis Bay, Duluth, MN and one upstream reference site. The biological impact of 51 chemicals detected in the surface water of 133 targeted chemicals was determined using biochemical endpoints, exposure activity ratios for biological and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge bases, and analysis of the covariance of ovary gene expression with surface water chemistry. Thirty-two chemicals were significantly linked by covariance with expressed genes. No estrogenic impact on biochemical endpoints was observed in male or female minnows. However, bisphenol A (BPA) was identified by chemical:gene covariation as the most impactful estrogenic chemical across all exposure sites. This was consistent with identification of estrogenic effects on gene expression, high BPA exposure activity ratios across all test sites, and historical analysis of the study area. Gene expression analysis also indicated the presence of nontargeted chemicals including chemotherapeutics consistent with a local hospital waste stream. Overall impacts on gene expression appeared to be related to changes in treatment plant function during rain events. This approach appears useful in examining the impacts of complex mixtures on fish and offers a potential route in linking chemical exposure to adverse outcomes that may reduce population sustainability.

Rapid effects of the aromatase inhibitor fadrozole on steroid production and gene expression in the ovary of female fathead minnows (Pimephales promelas).

Cytochrome P450 aromatase catalyzes conversion of C19 androgens to C18 estrogens and is critical for normal reproduction in female vertebrates. Fadrozole is a model aromatase inhibitor that has been shown to suppress estrogen production in the ovaries of fish. However, little is known about the early impacts of aromatase inhibition on steroid production and gene expression in fish. Adult female fathead minnows (Pimephales promelas) were exposed via water to 0, 5, or 50µg fadrozole/L for a time-course of 0.5, 1, 2, 4, and 6h, or 0 or 50µg fadrozole/L for a time-course of 6, 12, and 24h. We examined ex vivo ovarian 17ß-estradiol (E2) and testosterone (T) production, and plasma E2 concentrations from each study. Expression profiles of genes known or hypothesized to be impacted by fadrozole including aromatase (cytochrome P450 [cyp] 19a1a), steriodogenic acute regulatory protein (star), cytochrome P450 side-chain cleavage (cyp11a), cytochrome P450 17 alpha hydroxylase/17,20 lyase (cyp17), and follicle stimulating hormone receptor (fshr) were measured in the ovaries by quantitative real-time polymerase chain reaction (QPCR). In addition, broader ovarian gene expression was examined using a 15k fathead minnow microarray. The 5µg/L exposure significantly reduced ex vivo E2 production by 6h. In the 50µg/L treatment, ex vivo E2 production was significantly reduced after just 2h of exposure and remained depressed at all time-points examined through 24h. Plasma E2 concentrations were significantly reduced as early as 4h after initiation of exposure to either 5 or 50µg fadrozole/L and remained depressed throughout 24h in the 50µg/L exposure. Ex vivo T concentrations remained unchanged throughout the time-course. Expression of transcripts involved in steroidogenesis increased within the first 24h suggesting rapid induction of a mechanism to compensate for fadrozole inhibition of aromatase. Microarray results also showed fadrozole exposure caused concentration- and time-dependent changes in gene expression profiles in many HPG-axis pathways as early as 4h. This study provides insights into the very rapid effects of aromatase inhibition on steroidogenic processes in fish.

Predicting chemical bioavailability using microarray gene expression data and regression modeling: A tale of three explosive compounds.

BACKGROUND: Chemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation. RESULTS: This refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R(2) = 0.71-0.82 was achievable at a relatively low model complexity with as few as 3-10 predictor genes per model. These results are much more encouraging than our previous ones. CONCLUSION: This study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings.

Fish connectivity mapping: linking chemical stressors by their mechanisms of action-driven transcriptomic profiles.

BACKGROUND: A very large and rapidly growing collection of transcriptomic profiles in public repositories is potentially of great value to developing data-driven bioinformatics applications for toxicology/ecotoxicology. Modeled on human connectivity mapping (Cmap) in biomedical research, this study was undertaken to investigate the utility of an analogous Cmap approach in ecotoxicology. Over 3500 zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) transcriptomic profiles, each associated with one of several dozen chemical treatment conditions, were compiled into three distinct collections of rank-ordered gene lists (ROGLs) by species and microarray platforms. Individual query signatures, each consisting of multiple gene probes differentially expressed in a chemical condition, were used to interrogate the reference ROGLs. RESULTS: Informative connections were established at high success rates within species when, as defined by their mechanisms of action (MOAs), both query signatures and ROGLs were associated with the same or similar chemicals. Thus, a simple query signature functioned effectively as an exposure biomarker without need for a time-consuming process of development and validation. More importantly, a large reference database of ROGLs also enabled a query signature to cross-interrogate other chemical conditions with overlapping MOAs, leading to novel groupings and subgroupings of seemingly unrelated chemicals at a finer resolution. This approach confirmed the identities of several estrogenic chemicals, as well as a polycyclic aromatic hydrocarbon and a neuro-toxin, in the largely uncharacterized water samples near several waste water treatment plants, and thus demonstrates its future potential utility in real world applications. CONCLUSIONS: The power of Cmap should grow as chemical coverages of ROGLs increase, making it a framework easily scalable in the future. The feasibility of toxicity extrapolation across fish species using Cmap needs more study, however, as more gene expression profiles linked to chemical conditions common to multiple fish species are needed.

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility.

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure-property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation lgSw=kT+c, where Sw is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high-efficiency QSPR model. The performance of the model is assessed using cross-validation and external test set prediction. Predictive capacity of developed model is compared with COSMO-RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.

A weight of evidence assessment approach for adverse outcome pathways.

The adverse outcome pathway (AOP) is a framework to mechanistically link molecular initiating events to adverse biological outcomes. From a regulatory perspective, it is of crucial importance to determine the confidence for the AOP in question as well as the quality of data available in supporting this evaluation. A weight of evidence approach has been proposed for this task, but many of the existing frameworks for weight of evidence evaluation are qualitative and there is not clear guidance regarding how weight of evidence should be calculated for an AOP. In this paper we advocate the use of a subject matter expertise driven approach for weight of evidence evaluation based on criteria and metrics related to data quality and the strength of causal linkages between key events. As a demonstration, we notionally determine weight of evidence scores for two AOPs: Non-competitive ionotropic GABA receptor antagonism leading to epileptic seizures, and Antagonist-binding and stabilization of a co-repressor to the peroxisome proliferator-activated receptor α (PPARα) signaling complex ultimately causing starvation-like weight loss.

Daphnia magna's sense of competition: intra-specific interactions (ISI) alter life history strategies and increase metals toxicity.

This work investigates whether the scale-up to multi-animal exposures that is commonly applied in genomics studies provides equivalent toxicity outcomes to single-animal experiments of standard Daphnia magna toxicity assays. Specifically, we tested the null hypothesis that intraspecific interactions (ISI) among D. magna have neither effect on the life history strategies of this species, nor impact toxicological outcomes in exposure experiments with Cu and Pb. The results show that ISI significantly increased mortality of D. magna in both Cu and Pb exposure experiments, decreasing 14 day LC50 s and 95 % confidence intervals from 14.5 (10.9-148.3) to 8.4 (8.2-8.7) µg Cu/L and from 232 (156-4810) to 68 (63-73) µg Pb/L. Additionally, ISI potentiated Pb impacts on reproduction eliciting a nearly 10-fold decrease in the no-observed effect concentration (from 236 to 25 µg/L). As an indication of environmental relevance, the effects of ISI on both mortality and reproduction in Pb exposures were sustained at both high and low food rations. Furthermore, even with a single pair of Daphnia, ISI significantly increased (p < 0.05) neonate production in control conditions, demonstrating that ISI can affect life history strategy. Given these results we reject the null hypothesis and conclude that results from scale-up assays cannot be directly applied to observations from single-animal assessments in D. magna. We postulate that D. magna senses chemical signatures of conspecifics which elicits changes in life history strategies that ultimately increase susceptibility to metal toxicity.