Protective efficacy of a Zika purified inactivated virus vaccine candidate during pregnancy in marmosets

Zika virus (ZIKV) infection during pregnancy poses significant threats to maternal and fetal health, leading to intrauterine fetal demise and severe developmental malformations that constitute congenital Zika syndrome (CZS). As such, the development of a safe and effective ZIKV vaccine is a critical public health priority. However, the safety and efficacy of such a vaccine during pregnancy remain uncertain. Historically, the conduct of clinical trials in pregnant women has been challenging. Therefore, clinically relevant animal pregnancy models are in high demand for testing vaccine efficacy. We previously reported that a marmoset pregnancy model of ZIKV infection consistently demonstrated vertical transmission from mother to fetus during pregnancy. Using this marmoset model, we also showed that vertical transmission could be prevented by pre-pregnancy vaccination with Zika purified inactivated virus (ZPIV) vaccine. Here, we further examined the efficacy of ZPIV vaccination during pregnancy. Vaccination during pregnancy elicited virus neutralizing antibody responses that were comparable to those elicited by pre-pregnancy vaccination. Vaccination also reduced placental pathology, viral burden and vertical transmission of ZIKV during pregnancy, without causing adverse effects. These results provide key insights into the safety and efficacy of ZPIV vaccination during pregnancy and demonstrate positive effects of vaccination on the reduction of ZIKV infection, an important advance in preparedness for future ZIKV outbreaks.

Zika purified inactivated virus (ZPIV) vaccine reduced vertical transmission in pregnant immunocompetent mice

Zika virus (ZIKV) is a significant threat to pregnant women and their fetuses as it can cause severe birth defects and congenital neurodevelopmental disorders, referred to as congenital Zika syndrome (CZS). Thus, a safe and effective ZIKV vaccine for pregnant women to prevent in utero ZIKV infection is of utmost importance. Murine models of ZIKV infection are limited by the fact that immunocompetent mice are resistant to ZIKV infection. As such, interferon-deficient mice have been used in some preclinical studies to test the efficacy of ZIKV vaccine candidates against lethal virus challenge. However, interferon-deficient mouse models have limitations in assessing the immunogenicity of vaccines, necessitating the use of immunocompetent mouse pregnancy models. Using the human stat2 knock-in (hSTAT2KI) mouse pregnancy model, we show that vaccination with a purified formalin-inactivated Zika virus (ZPIV) vaccine prior to pregnancy successfully prevented vertical transmission. In addition, maternal immunity protected offspring against postnatal challenge for up to 28 days. Furthermore, passive transfer of human IgG purified from hyper-immune sera of ZPIV vaccinees prevented maternal and fetal ZIKV infection, providing strong evidence that the neutralizing antibody response may serve as a meaningful correlate of protection.

Impact of prior dengue virus infection on Zika virus infection during pregnancy in marmosets

Zika virus (ZIKV) infection during pregnancy causes severe developmental defects in newborns, termed congenital Zika syndrome (CZS). Factors contributing to a surge in ZIKV-associated CZS are poorly understood. One possibility is that ZIKV may exploit the antibody-dependent enhancement of infection mechanism, mediated by cross-reactive antibodies from prior dengue virus (DENV) infection, which may exacerbate ZIKV infection during pregnancy. In this study, we investigated the impact of prior DENV infection or no DENV infection on ZIKV pathogenesis during pregnancy in a total of four female common marmosets with five or six fetuses per group. The results showed that negative-sense viral RNA copies increased in the placental and fetal tissues of DENV-immune dams but not in DENV-naïve dams. In addition, viral proteins were prevalent in endothelial cells, macrophages, and neonatal Fc receptor-expressing cells in the placental trabeculae and in neuronal cells in the brains of fetuses from DENV-immune dams. DENV-immune marmosets maintained high titers of cross-reactive ZIKV-binding antibodies that were poorly neutralizing, raising the possibility that these antibodies might be involved in the exacerbation of ZIKV infection. These findings need to be verified in a larger study, and the mechanism involved in the exacerbation of ZIKV infection in DENV-immune marmosets needs further investigation. However, the results suggest a potential negative impact of preexisting DENV immunity on subsequent ZIKV infection during pregnancy in vivo.

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high-throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho-histone H3 biomarkers into a single-flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546-558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With "compound" serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule-based software program Derek Nexus, developed by Lhasa Limited. Compound sub-classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step-wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Evidence for an Aneugenic Mechanism of Action for Micronucleus Induction by Black Cohosh Extract

Black cohosh extract (BCE) is a popular botanical dietary supplement marketed to relieve symptoms of various gynecological ailments. Studies conducted by the National Toxicology Program (NTP) showed that BCE induces micronucleated erythrocytes in female rats and mice. Subsequently, the NTP showed that a variety of BCEs, including the sample that induced micronuclei (MN) in vivo ("NTP BCE") had a similar effect in human TK6 cells. Further testing with the MultiFlow® DNA Damage Assay revealed that TK6 cells exposed to NTP BCE, as well as a BCE reference material (BC XRM), exhibited a signature consistent with aneugenic activity in TK6 cells. Results from experiments reported herein confirmed these in vitro observations with NTP BCE and BC XRM. We extended these studies to include a novel test system, the MultiFlow Aneugen Molecular Mechanism Assay. For these experiments, TK6 cells were exposed to NTP BCE and BC XRM over a range of concentrations in the presence of fluorescent Taxol (488 Taxol). After 4 h, nuclei from lysed cells were stained with a nucleic acid dye and labeled with fluorescent antibodies against phospho-histone H3 (p-H3) and Ki-67. Whereas BCEs did not affect p-H3:Ki-67 ratios (a signature of aneugenic mitotic kinase inhibitors), 488 Taxol-associated fluorescence (a tubulin binder-sensitive endpoint) was affected. More specifically, 488 Taxol-associated fluorescence was reduced over the same concentration range that was previously observed to induce MN. These results provide direct evidence that BCEs destabilize microtubules in vitro, and this is the molecular mechanism responsible for the aneugenicity findings. Environ. Mol. Mutagen. 2019. © 2019 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.

Aneugen Molecular Mechanism Assay: Proof-of-Concept With 27 Reference Chemicals

A tiered bioassay and data analysis scheme is described for elucidating the most common molecular targets responsible for chemical-induced in vitro aneugenicity: tubulin destabilization, tubulin stabilization, and inhibition of mitotic kinase(s). To evaluate this strategy, TK6 cells were first exposed to each of 27 presumed aneugens over a range of concentrations. After 4 and 24 h of treatment, γH2AX, p53, phospho-histone H3 (p-H3), and polyploidization biomarkers were evaluated using the MultiFlow DNA Damage Assay Kit. The assay identified 27 of 27 chemicals as genotoxic, with 25 exhibiting aneugenic signatures, 1 aneugenic and clastogenic, and 1 clastogenic. Subsequently, a newly described follow-up assay was employed to investigate the aneugenic agents' molecular targets. For these experiments, TK6 cells were exposed to each of 26 chemicals in the presence of 488 Taxol. After 4 h, cells were lysed and the liberated nuclei and mitotic chromosomes were stained with a nucleic acid dye and labeled with fluorescent antibodies against p-H3 and Ki-67. Flow cytometric analyses revealed that alterations to 488 Taxol-associated fluorescence were only observed with tubulin binders-increases in the case of tubulin stabilizers, decreases with destabilizers. Mitotic kinase inhibitors with known Aurora kinase B inhibiting activity were the only aneugens that dramatically decreased the ratio of p-H3-positive to Ki-67-positive nuclei. Unsupervised hierarchical clustering based on 488 Taxol fluorescence and p-H3: Ki-67 ratios clearly distinguished compounds with these disparate molecular mechanisms. Furthermore, a classification algorithm based on an artificial neural network was found to effectively predict molecular target, as leave-one-out cross-validation resulted in 25/26 agreement with a priori expectations. These results are encouraging, as they suggest that an adequate number of training set chemicals, in conjunction with a machine learning algorithm based on 488 Taxol, p-H3, and Ki-67 responses, can reliably elucidate the most commonly encountered aneugenic molecular targets.

Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy

The in vitro MultiFlow® DNA Damage Assay multiplexes γH2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n = 40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were removed for analysis following 4 and 24 hr of treatment. The first data analysis step focused on chemicals' genotoxic potential, and for this purpose, we evaluated the performance of a machine learning (ML) ensemble, a rubric that considered fold increases in biomarkers against global evaluation factors (GEFs), and a hybrid strategy that considered ML and GEFs. This first tier further used ML output and/or GEFs to classify genotoxic activity as clastogenic and/or aneugenic. Test set results demonstrated the generalizability of the first tier, with particularly good performance from the ML ensemble: 35/40 (88%) concordance with a priori genotoxicity expectations and 21/24 (88%) agreement with expected mode of action (MoA). A second tier applied unsupervised hierarchical clustering to the biomarker response data, and these analyses were found to group certain chemicals, especially aneugens, according to their molecular targets. Finally, a third tier utilized benchmark dose analyses and MultiFlow biomarker responses to rank genotoxic potency. The relevance of these rankings is supported by the strong agreement found between benchmark dose values derived from MultiFlow biomarkers compared to those generated from parallel in vitro micronucleus analyses. Collectively, the results suggest that a tiered MultiFlow data analysis pipeline is capable of rapidly and effectively identifying genotoxic hazards while providing additional information that is useful for modern risk assessments-MoA, molecular targets, and potency. Environ. Mol. Mutagen. 60:513-533, 2019. © 2019 Wiley Periodicals, Inc.

Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals

The in vitro MultiFlow DNA Damage assay multiplexes p53, γH2AX, phospho-histone H3, and polyploidization biomarkers into 1 flow cytometric analysis (Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016). Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach. Environ. Mol. Mutagen. 57, 171-189). The work reported herein evaluated the generalizability of the method, as well as several data analytics strategies, to a range of chemical classes not studied previously. TK6 cells were exposed to each of 103 diverse chemicals, 86 of which were supplied by the National Toxicology Program (NTP) and selected based upon responses in genetic damage assays conducted under the Tox21 program. Exposures occurred for 24 h over a range of concentrations, and cell aliquots were removed at 4 and 24 h for analysis. Multiplexed response data were evaluated using 3 machine learning models designed to predict genotoxic mode of action based on data from a training set of 85 previously studied chemicals. Of 54 chemicals with sufficient information to make an a priori call on genotoxic potential, the prediction models' accuracies were 79.6% (random forest), 88.9% (logistic regression), and 90.7% (artificial neural network). A majority vote ensemble of the 3 models provided 92.6% accuracy. Forty-nine NTP chemicals were not adequately tested (maximum concentration did not approach assay's cytotoxicity limit) and/or had insufficient conventional genotoxicity data to allow their genotoxic potential to be defined. For these chemicals MultiFlow data will be useful in future research and hypothesis testing. Collectively, the results suggest the MultiFlow assay and associated data analysis strategies are broadly generalizable, demonstrating high predictability when applied to new chemicals and classes of compounds.

Interlaboratory evaluation of a multiplexed high information content in vitro genotoxicity assay

We previously described a multiplexed in vitro genotoxicity assay based on flow cytometric analysis of detergent-liberated nuclei that are simultaneously stained with propidium iodide and labeled with fluorescent antibodies against p53, γH2AX, and phospho-histone H3. Inclusion of a known number of microspheres provides absolute nuclei counts. The work described herein was undertaken to evaluate the interlaboratory transferability of this assay, commercially known as MultiFlow^® DNA Damage Kit-p53, γH2AX, Phospho-Histone H3. For these experiments, seven laboratories studied reference chemicals from a group of 84 representing clastogens, aneugens, and nongenotoxicants. TK6 cells were exposed to chemicals in 96-well plates over a range of concentrations for 24 hr. At 4 and 24 hr, cell aliquots were added to the MultiFlow reagent mix and following a brief incubation period flow cytometric analysis occurred, in most cases directly from a 96-well plate via a robotic walk-away data acquisition system. Multiplexed response data were evaluated using two analysis approaches, one based on global evaluation factors (i.e., cutoff values derived from all interlaboratory data), and a second based on multinomial logistic regression that considers multiple biomarkers simultaneously. Both data analysis strategies were devised to categorize chemicals as predominately exhibiting a clastogenic, aneugenic, or nongenotoxic mode of action (MoA). Based on the aggregate 231 experiments that were performed, assay sensitivity, specificity, and concordance in relation to a priori MoA grouping were ≥ 92%. These results are encouraging as they suggest that two distinct data analysis strategies can rapidly and reliably predict new chemicals' predominant genotoxic MoA based on data from an efficient and transferable multiplexed in vitro assay. Environ. Mol. Mutagen. 58:146-161, 2017. © 2017 Wiley Periodicals, Inc.

γH2AX and p53 responses in TK6 cells discriminate promutagens and nongenotoxicants in the presence of rat liver S9

Previous work with a diverse set of reference chemicals suggests that an in vitro multiplexed flow cytometry-based assay (MultiFlow™ DNA Damage Kit-p53, γH2AX, Phospho-Histone H3) can distinguish direct-acting clastogens and aneugens from nongenotoxicants (Bryce SM et al. []: Environ Mol Mutagen 57:171-189). This work extends this line of investigation to include compounds that require metabolic activation to form reactive electrophiles. For these experiments, TK6 cells were exposed to 11 promutagens and 37 presumed nongenotoxicants in 96 well plates. Unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. Exposure occurred for 4 hr after which time cells were washed to remove S9 and test article. Immediately following the wash and again at 24 hr, cell aliquots were added to wells of a microtiter plate containing the working detergent/stain/antibody cocktail. After a brief incubation, robotic sampling was employed for walk-away flow cytometric data acquisition. Univariate logistic regression analyses indicated that γH2AX induction and p53 activation provide the greatest degree of discrimination between clastogens and nongenotoxicants. Multivariate prediction algorithms that incorporated both of these endpoints, in each combination of time points, were evaluated. The best performing models correctly predicted 9 clastogens out of 11 and 36 nongenotoxicants out of 37. These results are encouraging as they suggest that an efficient and highly scalable multiplexed assay can effectively identify clastogenic chemicals that require bioactivation. More work is planned with a broader range of chemicals, additional cell lines, and other laboratories to further evaluate the merits and limitations of this approach. Environ. Mol. Mutagen. 57:546-558, 2016. © 2016 Wiley Periodicals, Inc.

Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach

Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium iodide to serve as a pan-DNA dye, fluorescent antibodies against γH2AX, phospho-histone H3, and p53, and fluorescent microspheres for absolute nuclei counts. The assay was applied to TK6 cells and 67 diverse reference chemicals that served as a training set. Exposure was for 24 hrs in 96-well plates, and unless precipitation or foreknowledge about cytotoxicity suggested otherwise, the highest concentration was 1 mM. At 4- and 24-hrs aliquots were removed and added to microtiter plates containing the reagent mix. Following a brief incubation period robotic sampling facilitated walk-away data acquisition. Univariate analyses identified biomarkers and time points that were valuable for classifying agents into one of three groups: clastogenic, aneugenic, or non-genotoxic. These mode of action predictions were optimized with a forward-stepping process that considered Wald test p-values, receiver operator characteristic curves, and pseudo R(2) values, among others. A particularly high performing multinomial logistic regression model was comprised of four factors: 4 hr γH2AX and phospho-histone H3 values, and 24 hr p53 and polyploidy values. For the training set chemicals, the four-factor model resulted in 94% concordance with our a priori classifications. Cross validation occurred via a leave-one-out approach, and in this case 91% concordance was observed. A test set of 17 chemicals that were not used to construct the model were evaluated, some of which utilized a short-term treatment in the presence of a metabolic activation system, and in 16 cases mode of action was correctly predicted. These initial results are encouraging as they suggest a machine learning strategy can be used to rapidly and reliably predict new chemicals' genotoxic mode of action based on data from an efficient and highly scalable multiplexed assay.