A Systems Biology Approach Reveals Converging Molecular Mechanisms that Link Different POPs to Common Metabolic Diseases.

BACKGROUND: A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published. OBJECTIVES: We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs [2,3,7,8-tetrachlorodibenzodioxin (TCDD), 2,2´,4,4´,5,5´-hexachlorobiphenyl (PCB 153), and 4,4´-dichlorodiphenyldichloroethylene (p,p´-DDE)] were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases. METHODS: For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes. CONCLUSIONS: Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies. CITATION: Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034-1041; http://dx.doi.org/10.1289/ehp.1510308.

Chronophin coordinates cell leading edge dynamics by controlling active cofilin levels.

Cofilin, a critical player of actin dynamics, is spatially and temporally regulated to control the direction and force of membrane extension required for cell locomotion. In carcinoma cells, although the signaling pathways regulating cofilin activity to control cell direction have been established, the molecular machinery required to generate the force of the protrusion remains unclear. We show that the cofilin phosphatase chronophin (CIN) spatiotemporally regulates cofilin activity at the cell edge to generate persistent membrane extension. We show that CIN translocates to the leading edge in a PI3-kinase-, Rac1-, and cofilin-dependent manner after EGF stimulation to activate cofilin, promotes actin free barbed end formation, accelerates actin turnover, and enhances membrane protrusion. In addition, we establish that CIN is crucial for the balance of protrusion/retraction events during cell migration. Thus, CIN coordinates the leading edge dynamics by controlling active cofilin levels to promote MTLn3 cell protrusion.

Low-Dose Cadmium Causes Metabolic and Genetic Dysregulation Associated With Fatty Liver Disease in Mice.

Cadmium (Cd) is present in food at low levels and accumulates in humans throughout life because it is not effectively excreted. Cd from smoking or occupational exposure shows adverse effects on health, but the mechanistic effect of Cd at low dietary intake levels is poorly studied. Epidemiology studies found that nonalcoholic fatty liver disease (NAFLD), common in U.S. adults, is associated with Cd burden. In cell studies, we found that environmental low-dose Cd oxidized proteins and stimulated inflammatory signaling. However, little is known about low-dose Cd effects on liver function and associated metabolic pathways in vivo. We investigated effects of low-level Cd exposure on liver gene transcripts, metabolites, and associated metabolic pathways and function after challenging mice with Cd (10 mg/l) by drinking water. Results showed liver Cd in treated mice was similar to adult humans without occupational or smoking exposures and 10-fold higher than control mouse values. Pathway analysis of significantly altered liver genes and metabolites mapped to functional pathways of lipid metabolism, cell death and mitochondrial oxidative phosphorylation. These are well-recognized pathways associated with NAFLD. Cd-treated mice had higher liver enzymes in plasma and a trend toward fat accumulation in liver. To verify low-dose Cd-induced stimulation of cell death pathways, phosphorylation of c-Jun N-terminal kinase (JNK) was examined in cultured hepatic cells. Consistent with mouse liver data, low-dose Cd stimulated JNK activation. Together, the results show that low-dose Cd exposure causes liver function changes consistent with a role in NAFLD and possibly also nonalcoholic steatohepatitis.

Biomarkers in toxicology and risk assessment.

Over the last 30 years, the field of biomarkers has greatly expanded as early and specific endpoints for monitoring cellular responses to various disease states and exposures to drugs and chemical agents. They have enjoyed some success as predictors of health outcomes for a number of clinical diseases, but the application to chemical exposure risk assessments has been more limited. Biomarkers may be classified into categories of markers of exposure, effect, and susceptibility. Currently, "omics" biomarkers (i.e., genomic, proteomic, and metabolomic/metabonomic) are the major classes of biomarkers under development. These markers represent a continuum of cellular responses to drug or chemical exposures and provide linkages to mechanisms of cell injury/cell death or carcinogenic transformation. On the other hand, translation and application of these biomarkers for risk assessment has been limited due to validation and interpretation issues that need to be addressed in order for these potentially extremely valuable endpoints to reach their full potential as predictive tools for public health. This short chapter will briefly review these three "omics" biomarker classes and examine some validation/translation aspects needed in order for them to reach their full potential and acceptance as valuable tools for application to risk assessment.

Type I signal peptidase and protein secretion in Staphylococcus aureus.

Staphylococcus aureus is an important human pathogen whose virulence relies on the secretion of many different proteins. In general, the secretion of most proteins in S. aureus, as well as other bacteria, is dependent on the type I signal peptidase (SPase)-mediated cleavage of the N-terminal signal peptide that targets a protein to the general secretory pathway. The arylomycins are a class of natural product antibiotics that inhibit SPase, suggesting that they may be useful chemical biology tools for characterizing the secretome. While wild-type S. aureus (NCTC 8325) is naturally resistant to the arylomycins, sensitivity is conferred via a point mutation in its SPase. Here, we use a synthetic arylomycin along with a sensitized strain of S. aureus and multidimensional protein identification technology (MudPIT) mass spectrometry to identify 46 proteins whose extracellular accumulation requires SPase activity. Forty-four possess identifiable Sec-type signal peptides and thus are likely canonically secreted proteins, while four also appear to possess cell wall retention signals. We also identified the soluble C-terminal domains of two transmembrane proteins, lipoteichoic acid synthase, LtaS, and O-acyteltransferase, OatA, both of which appear to have noncanonical, internal SPase cleavage sites. Lastly, we identified three proteins, HtrA, PrsA, and SAOUHSC_01761, whose secretion is induced by arylomycin treatment. In addition to elucidating fundamental aspects of the physiology and pathology of S. aureus, the data suggest that an arylomycin-based therapeutic would reduce virulence while simultaneously eradicating an infection.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Synthesis and characterization of dimethacrylates containing quaternary ammonium functionalities for dental applications.

OBJECTIVES: The widespread incidence of recurrent caries highlights the need for improved dental restorative materials. The objective of this study was to synthesize low viscosity ionic dimethacrylate monomers (IDMAs) that contain quaternary ammoniums groups (antimicrobial functionalities) and are compatible with existing dental dimethacrylate-based monomers. Such monomers have the potential to copolymerize with other methacrylate monomers and produce antibacterial polymers. METHODS: Two monomers (IDMA-1 and IDMA-2) were synthesized using the Menschutkin reaction and incorporated at 0-30% (by mass) into a 1:1 (by mass) bisphenol A glycerolate dimethacrylate (BisGMA):triethylene glycol dimethacrylate (TEGDMA) resin. Resin viscosity was quantified using rheology, and polymer degree of conversion (DC) and surface charge density were measured using Fourier transform infrared spectroscopy (FTIR) and fluorescein binding, respectively. Effects of IDMA-1 on initial attachment of Streptococcus mutans and on viability and metabolic activity (via reductase enzymes) of RAW 264.7 macrophage-like cells were quantified. RESULTS: IDMA-1 and IDMA-2 were prepared and characterized. IDMA-1 was miscible with BisGMA:TEGDMA and slightly increased the resin viscosity and DC. As expected, polymeric surface charge density increased with increasing IDMA-1. Incorporation of 10% IDMA-1 into BisGMA:TEGDMA reduced bacterial colonization without affecting viability or metabolic activity of mammalian cells. Increasing IDMA-1 up to 30% had no additional effect on bacterial coverage, but ≥20% IDMA-1 significantly reduced macrophage density, viability, and metabolic activity. Leachables from polymers containing IDMA-1 were not cytotoxic. SIGNIFICANCE: The Menschutkin reaction provides a facile, convenient means to synthesize new monomers with quaternary ammonium groups for dental and medical applications.

Metal ions affecting the kidney.

This chapter provides a succinct summary of the nephrotoxic effects of a number of metals/metalloids on an individual or mixture basis. There is a discussion of routes of exposure, mechanisms of uptake by renal cells and the potential impact of nanomaterials on these processes. An emphasis is placed on the toxicity of these metals/ metalloids to individual cell types in the kidney and the application of biomarkers for the early detection of kidney cell injury prior to the onset of an overt clinical state such as end-stage renal disease. The issue of interactions between nephrotoxic metals in mixture exposures is discussed in relation to the application of molecular biomarkers for early detection of renal cell injury.

Metal ions affecting the hematological system.

Many metals are essential elements and necessary for proper biological function at low intake levels. However, exposure to high intake levels of these metals may result in adverse effects. In addition, exposures to mixtures of metals may produce interactions that result in synergistic or antagonistic effects. This chapter focuses on metals that affect the hematological system and how exposures to mixtures of metals may contribute to their hematotoxicity. Exposure to arsenic, cadmium, copper, lead, mercury, tin or zinc has been shown to produce some effect on the hematological system. Binary interactions resulting from exposure to combinations of metals may increase or decrease the hematotoxicity induced by individual metals. For example, copper, iron, and zinc have been shown to have a protective effect on the hematotoxicity of lead. In contrast, co-exposure to manganese may increase the hematotoxicity of lead.

Type I signal peptidase and protein secretion in Staphylococcus epidermidis.

Bacterial protein secretion is a highly orchestrated process that is essential for infection and virulence. Despite extensive efforts to predict or experimentally detect proteins that are secreted, the characterization of the bacterial secretome has remained challenging. A central event in protein secretion is the type I signal peptidase (SPase)-mediated cleavage of the N-terminal signal peptide that targets a protein for secretion via the general secretory pathway, and the arylomycins are a class of natural products that inhibit SPase, suggesting that they may be useful chemical biology tools for characterizing the secretome. Here, using an arylomycin derivative, along with two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identify 11 proteins whose secretion from stationary-phase Staphylococcus epidermidis is dependent on SPase activity, 9 of which are predicted to be translated with canonical N-terminal signal peptides. In addition, we find that the presence of extracellular domains of lipoteichoic acid synthase (LtaS) and the ß-lactam response sensor BlaR1 in the medium is dependent on SPase activity, suggesting that they are cleaved at noncanonical sites within the protein. In all, the data define the proteins whose stationary-phase secretion depends on SPase and also suggest that the arylomycins should be valuable chemical biology tools for the study of protein secretion in a wide variety of different bacteria.

Exposure to Pb, Cd, and As mixtures potentiates the production of oxidative stress precursors: 30-day, 90-day, and 180-day drinking water studies in rats.

Exposure to chemical mixtures is a common and important determinant of toxicity and is of particular concern due to their appearance in sources of drinking water. Despite this, few in vivo mixture studies have been conducted to date to understand the health impact of chemical mixtures compared to single chemicals. Interactive effects of lead (Pb), cadmium (Cd) and arsenic (As) were evaluated in 30-, 90-, and 180-day factorial design drinking water studies in rats designed to test the hypothesis that ingestion of such mixtures at individual component Lowest-Observed-Effect-Levels (LOELs) results in increased levels of the pro-oxidant delta aminolevulinic acid (ALA), iron, and copper. LOEL levels of Pb, Cd, and As mixtures resulted in the increased presence of mediators of oxidative stress such as ALA, copper, and iron. ALA increases were followed by statistically significant increases in kidney copper in the 90- and 180-day studies. Statistical evidence of interaction was identified for six biologically relevant variables: blood delta aminolevulinic acid dehydratase (ALAD), kidney ALAD, urinary ALA, urinary iron, kidney iron, and kidney copper. The current investigations underscore the importance of considering interactive effects that common toxic agents such as Pb, Cd, and As may have upon one another at low-dose levels. The interactions between known toxic trace elements at biologically relevant concentrations shown here demonstrate a clear need to rigorously review methods by which national/international agencies assess health risks of chemicals, since exposures may commonly occur as complex mixtures.

SAR/QSAR methods in public health practice.

Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

Research recommendations for selected IARC-classified agents.

OBJECTIVES: There are some common occupational agents and exposure circumstances for which evidence of carcinogenicity is substantial but not yet conclusive for humans. Our objectives were to identify research gaps and needs for 20 agents prioritized for review based on evidence of widespread human exposures and potential carcinogenicity in animals or humans. DATA SOURCES: For each chemical agent (or category of agents), a systematic review was conducted of new data published since the most recent pertinent International Agency for Research on Cancer (IARC) Monograph meeting on that agent. DATA EXTRACTION: Reviewers were charged with identifying data gaps and general and specific approaches to address them, focusing on research that would be important in resolving classification uncertainties. An expert meeting brought reviewers together to discuss each agent and the identified data gaps and approaches. DATA SYNTHESIS: Several overarching issues were identified that pertained to multiple agents; these included the importance of recognizing that carcinogenic agents can act through multiple toxicity pathways and mechanisms, including epigenetic mechanisms, oxidative stress, and immuno- and hormonal modulation. CONCLUSIONS: Studies in occupational populations provide important opportunities to understand the mechanisms through which exogenous agents cause cancer and intervene to prevent human exposure and/or prevent or detect cancer among those already exposed. Scientific developments are likely to increase the challenges and complexities of carcinogen testing and evaluation in the future, and epidemiologic studies will be particularly critical to inform carcinogen classification and risk assessment processes.

Interpreting NHANES biomonitoring data, cadmium.

Cadmium (Cd) occurs naturally in the environment and the general population's exposure to it is predominantly through diet. Chronic Cd exposure is a public health concern because Cd is a known carcinogen; it accumulates in the body and causes kidney damage. The National Health and Nutritional Examination Survey (NHANES) has measured urinary Cd; the 2003-2004 NHANES survey cycle reported estimates for 2257 persons aged 6 years and older in the Fourth National Report on Human Exposure to Environmental Chemicals. As part of translational research to make computerized models accessible to health risk assessors we re-coded a cadmium model in Berkeley Madonna simulation language. This model was used in our computational toxicology laboratory to predict the urinary excretion of cadmium. The model simulated the NHANES-measured data very well from ages 6 to 60+ years. An unusual increase in Cd urinary excretion was observed among 6-11-year-olds, followed by a continuous monotonic rise into the seventh decade of life. This observation was also made in earlier studies that could be life stage-related and a function of anatomical and phsysiological changes occurring during this period of life. Urinary excretion of Cd was approximately twofold higher among females than males in all age groups. The model describes Cd's cumulative nature in humans and accommodates the observed variation in exposure/uptake over the course of a lifetime. Such models may be useful for interpreting biomonitoring data and risk assessment.

A Bayesian network model for biomarker-based dose response.

A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison. The network-derived benchmark concentration was approximately an order of magnitude lower than that from the usual exposure concentration versus response approach, which suggests that the presence of more information in the low-dose region (where changes in biomarkers are detectable but effects on AML mortality are not) helps inform the description of the AML response at lower exposures. This work provides a quantitative approach for linking changes in biomarkers of effect both to exposure information and to changes in disease response. Such linkage can provide a scientifically valid point of departure that incorporates precursor dose-response information without being dependent on the difficult issue of a definition of adversity for precursors.

Modification by ALAD of the association between blood lead and blood pressure in the U.S. population: results from the Third National Health and Nutrition Examination Survey.

BACKGROUND: Environmental lead exposure has been found to be associated with an increased risk of hypertension. Individuals vary greatly in susceptibility to lead toxicity, and genetic susceptibility has often been cited as the probable cause for such variation. OBJECTIVE: The main objective is to determine the role of the aminolevulinic acid dehydratase (ALAD) gene, which encodes the main carrier protein of lead in blood, in the association between lead exposure and blood pressure (BP) and hypertension in the U.S. population. METHODS: We analyzed data from individuals >or= 17 years of age who participated in the Third National Health and Nutrition Examination Survey for whom DNA was available (n = 6,016). Multivariable logistic and linear regressions stratified by race/ethnicity were used to examine whether hypertension and BP were associated with ALAD and blood lead levels (BLL). RESULTS: BLL was associated with systolic BP in non-Hispanic whites and with hypertension and systolic and diastolic BP in non-Hispanic blacks. BLL was not associated with BP outcomes in Mexican Americans. Non-Hispanic white ALAD2 carriers in the highest BLL quartile (3.852.9 microg/dL) had a significantly higher adjusted prevalence odds ratio for hypertension compared with ALAD1 homozygous individuals. We also found a significant interaction between lead concentration and the ALAD2 allele in non-Hispanic whites and non-Hispanic blacks in relation to systolic BP. CONCLUSIONS: BLL may be an important risk factor for hypertension and increased systolic and diastolic BP. These associations may be modified by ALAD genotype.

Novel p47(phox)-related organizers regulate localized NADPH oxidase 1 (Nox1) activity.

The mechanisms that determine localized formation of reactive oxygen species (ROS) through NADPH (reduced form of nicotinamide adenine dinucleotide phosphate) oxidase (Nox) family members in nonphagocytic cells are unknown. We show that the c-Src substrate proteins Tks4 (tyrosine kinase substrate with four SH3 domains) and Tks5 are functional members of a p47(phox)-related organizer superfamily. Tks proteins selectively support Nox1 and Nox3 (and not Nox2 and Nox4) activity in reconstituted cellular systems and interact with the NoxA1 activator protein through an Src homology 3 domain-mediated interaction. Endogenous Tks4 is required for Rac guanosine triphosphatase- and Nox1-dependent ROS production by DLD1 colon cancer cells. Our results are consistent with the Tks-mediated recruitment of Nox1 to invadopodia that form in DLD1 cells in a Tks- and Nox-dependent fashion. We propose that Tks organizers represent previously unrecognized members of an organizer superfamily that link Nox to localized ROS formation.