Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple-stressor experiments.

Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.

Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises.

Understanding how multiple co-occurring environmental stressors combine to affect biodiversity and ecosystem services is an on-going grand challenge for ecology. Currently, progress has been made through accumulating large numbers of smaller-scale empirical studies that are then investigated by meta-analyses to detect general patterns. There is particular interest in detecting, understanding and predicting 'ecological surprises' where stressors interact in a non-additive (e.g. antagonistic or synergistic) manner, but so far few general results have emerged. However, the ability of the statistical tools to recover non-additive interactions in the face of data uncertainty is unstudied, so crucially, we do not know how well the empirical results reflect the true stressor interactions. Here, we investigate the performance of the commonly implemented additive null model. A meta-analysis of a large (545 interactions) empirical dataset for the effects of pairs of stressors on freshwater communities reveals additive interactions dominate individual studies, whereas pooling the data leads to an antagonistic summary interaction class. However, analyses of simulated data from food chain models, where the underlying interactions are known, suggest both sets of results may be due to observation error within the data. Specifically, we show that the additive null model is highly sensitive to observation error, with non-additive interactions being reliably detected at only unrealistically low levels of data uncertainty. Similarly, plausible levels of observation error lead to meta-analyses reporting antagonistic summary interaction classifications even when synergies co-dominate. Therefore, while our empirical results broadly agree with those of previous freshwater meta-analyses, we conclude these patterns may be driven by statistical sampling rather than any ecological mechanisms. Further investigation of candidate null models used to define stressor-pair interactions is essential, and once any artefacts are accounted for, the so-called 'ecological surprises' may be more frequent than was previously assumed.

Cloning, expression, purification, crystallization and preliminary X-ray diffraction studies of N-acetylneuraminate lyase from methicillin-resistant Staphylococcus aureus.

The enzyme N-acetylneuraminate lyase (EC 4.1.3.3) is involved in the metabolism of sialic acids. Specifically, the enzyme catalyzes the retro-aldol cleavage of N-acetylneuraminic acid to form N-acetyl-D-mannosamine and pyruvate. Sialic acids comprise a large family of nine-carbon amino sugars, all of which are derived from the parent compound N-acetylneuraminic acid. In recent years, N-acetylneuraminate lyase has received considerable attention from both mechanistic and structural viewpoints and has been recognized as a potential antimicrobial drug target. The N-acetylneuraminate lyase gene was cloned from methicillin-resistant Staphylococcus aureus genomic DNA, and recombinant protein was expressed and purified from Escherichia coli BL21 (DE3). The enzyme crystallized in a number of crystal forms, predominantly from PEG precipitants, with the best crystal diffracting to beyond 1.70 Šresolution in space group P21. Molecular replacement indicates the presence of eight monomers per asymmetric unit. Understanding the structural biology of N-acetylneuraminate lyase in pathogenic bacteria, such as methicillin-resistant S. aureus, will provide insights for the development of future antimicrobials.

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non-null stressor-pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user-defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system-specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

Longitudinal Study of Circulating Biomarkers in Patients with Resectable Pancreatic Ductal Adenocarcinoma.

While patients with resectable pancreatic ductal adenocarcinoma (PDAC) show improved survival compared to their non-resectable counterparts, survival remains low owing to occult metastatic disease and treatment resistance. Liquid biopsy based on circulating tumor cells (CTCs) and cell-free DNA (cfDNA) has been shown to predict recurrence and treatment resistance in various types of cancers, but their utility has not been fully demonstrated in resectable PDAC. We have simultaneously tracked three circulating biomarkers, including CTCs, cfDNA, and circulating tumor DNA (ctDNA), over a period of cancer treatment using a microfluidic device and droplet digital PCR (ddPCR). The microfluidic device is based on the combination of filtration and immunoaffinity mechanisms. We have measured CTCs, cfDNA, and ctDNA in a cohort of seven resectable PDAC patients undergoing neoadjuvant therapy followed by surgery, and each patient was followed up to 10 time points over a period of 4 months. CTCs were detectable in all patients (100%) at some point during treatment but were detectable in only three out of six patients (50%) prior to the start of treatment. Median cfDNA concentrations remained comparable to negative controls throughout treatment. ddPCR was able to find KRAS mutations in six of seven patients (86%); however, these mutations were present in only two of seven patients (29%) prior to treatment. Overall, the majority of circulating biomarkers (81% for CTCs and 91% for cfDNA/ctDNA) were detected after the start of neoadjuvant therapy but before surgery. This study suggests that a longitudinal study of circulating biomarkers throughout treatment provides more useful information than those single time-point tests for resectable PDAC patients.

Inhibiting dihydrodipicolinate synthase across species: towards specificity for pathogens?

Dihydrodipicolinate synthase (DHDPS) is a key enzyme in lysine biosynthesis and an important antibiotic target. The specificity of a range of heterocyclic product analogues against DHDPS from three pathogenic species, Bacillus anthracis, Mycobacterium tuberculosis and methicillin-resistant Staphylococcus aureus, and the evolutionarily related N-acetylneuraminate lyase, has been determined. The results suggest that the development of species-specific inhibitors of DHDPS as potential antibacterials is achievable.

Purification, crystallization and preliminary X-ray diffraction studies to near-atomic resolution of dihydrodipicolinate synthase from methicillin-resistant Staphylococcus aureus.

In recent years, dihydrodipicolinate synthase (DHDPS; EC 4.2.1.52) has received considerable attention from both mechanistic and structural viewpoints. DHDPS is part of the diaminopimelate pathway leading to lysine, coupling (S)-aspartate-beta-semialdehyde with pyruvate via a Schiff base to a conserved active-site lysine. In this paper, the cloning, expression, purification, crystallization and preliminary X-ray diffraction analysis of DHDPS from methicillin-resistant Staphylococcus aureus, an important bacterial pathogen, are reported. The enzyme was crystallized in a number of forms, predominantly from PEG precipitants, with the best crystal diffracting to beyond 1.45 A resolution. The space group was P1 and the unit-cell parameters were a = 65.4, b = 67.6, c = 78.0 A, alpha = 90.1, beta = 68.9, gamma = 72.3 degrees . The crystal volume per protein weight (V(M)) was 2.34 A(3) Da(-1), with an estimated solvent content of 47% for four monomers per asymmetric unit. The structure of the enzyme will help to guide the design of novel therapeutics against the methicillin-resistant S. aureus pathogen.

Structure and evolution of a novel dimeric enzyme from a clinically important bacterial pathogen.

Dihydrodipicolinate synthase (DHDPS) catalyzes the first committed step of the lysine biosynthetic pathway. The tetrameric structure of DHDPS is thought to be essential for enzymatic activity, as isolated dimeric mutants of Escherichia coli DHDPS possess less than 2.5% that of the activity of the wild-type tetramer. It has recently been proposed that the dimeric form lacks activity due to increased dynamics. Tetramerization, by buttressing two dimers together, reduces dynamics in the dimeric unit and explains why all active bacterial DHDPS enzymes to date have been shown to be homo-tetrameric. However, in this study we demonstrate for the first time that DHDPS from methicillin-resistant Staphylococcus aureus (MRSA) exists in a monomer-dimer equilibrium in solution. Fluorescence-detected analytical ultracentrifugation was employed to show that the dimerization dissociation constant of MRSA-DHDPS is 33 nm in the absence of substrates and 29 nm in the presence of (S)-aspartate semialdehyde (ASA), but is 20-fold tighter in the presence of the substrate pyruvate (1.6 nm). The MRSA-DHDPS dimer exhibits a ping-pong kinetic mechanism (k(cat)=70+/-2 s(-1), K(m)(Pyruvate)=0.11+/-0.01 mm, and K(m)(ASA)=0.22+/-0.02 mm) and shows ASA substrate inhibition with a K(si)(ASA) of 2.7+/-0.9 mm. We also demonstrate that unlike the E. coli tetramer, the MRSA-DHDPS dimer is insensitive to lysine inhibition. The near atomic resolution (1.45 A) crystal structure confirms the dimeric quaternary structure and reveals that the dimerization interface of the MRSA enzyme is more extensive in buried surface area and noncovalent contacts than the equivalent interface in tetrameric DHDPS enzymes from other bacterial species. These data provide a detailed mechanistic insight into DHDPS catalysis and the evolution of quaternary structure of this important bacterial enzyme.