An age-progressive platelet differentiation path from hematopoietic stem cells causes exacerbated thrombosis.

Platelet dysregulation is drastically increased with advanced age and contributes to making cardiovascular disorders the leading cause of death of elderly humans. Here, we reveal a direct differentiation pathway from hematopoietic stem cells into platelets that is progressively propagated upon aging. Remarkably, the aging-enriched platelet path is decoupled from all other hematopoietic lineages, including erythropoiesis, and operates as an additional layer in parallel with canonical platelet production. This results in two molecularly and functionally distinct populations of megakaryocyte progenitors. The age-induced megakaryocyte progenitors have a profoundly enhanced capacity to engraft, expand, restore, and reconstitute platelets in situ and upon transplantation and produce an additional platelet population in old mice. The two pools of co-existing platelets cause age-related thrombocytosis and dramatically increased thrombosis in vivo. Strikingly, aging-enriched platelets are functionally hyper-reactive compared with the canonical platelet populations. These findings reveal stem cell-based aging as a mechanism for platelet dysregulation and age-induced thrombosis.

Decreased levels of discomfort in repeatedly handled mice during experimental procedures, assessed by facial expressions.

Mice are the most commonly used laboratory animal, yet there are limited studies which investigate the effects of repeated handling on their welfare and scientific outcomes. Furthermore, simple methods to evaluate distress in mice are lacking, and specialized behavioral or biochemical tests are often required. Here, two groups of CD1 mice were exposed to either traditional laboratory handling methods or a training protocol with cup lifting for 3 and 5 weeks. The training protocol was designed to habituate the mice to the procedures involved in subcutaneous injection, e.g., removal from the cage, skin pinch. This protocol was followed by two common research procedures: subcutaneous injection and tail vein blood sampling. Two training sessions and the procedures (subcutaneous injection and blood sampling) were video recorded. The mouse facial expressions were then scored, focusing on the ear and eye categories of the mouse grimace scale. Using this assessment method, trained mice expressed less distress than the control mice during subcutaneous injection. Mice trained for subcutaneous injection also had reduced facial scores during blood sampling. We found a clear sex difference as female mice responded to training faster than the male mice, they also had lower facial scores than the male mice when trained. The ear score appeared to be a more sensitive measure of distress than the eye score, which may be more indicative of pain. In conclusion, training is an important refinement method to reduce distress in mice during common laboratory procedures and this can best be assessed using the ear score of the mouse grimace scale.

New transgenic mouse models enabling pan-hematopoietic or selective hematopoietic stem cell depletion in vivo.

Hematopoietic stem cell (HSC) multipotency and self-renewal are typically defined through serial transplantation experiments. Host conditioning is necessary for robust HSC engraftment, likely by reducing immune-mediated rejection and by clearing limited HSC niche space. Because irradiation of the recipient mouse is non-specific and broadly damaging, there is a need to develop alternative models to study HSC performance at steady-state and in the absence of radiation-induced stress. We have generated and characterized two new mouse models where either all hematopoietic cells or only HSCs can be specifically induced to die in vivo or in vitro. Hematopoietic-specific Vav1-mediated expression of a loxP-flanked diphtheria-toxin receptor (DTR) renders all hematopoietic cells sensitive to diphtheria toxin (DT) in "Vav-DTR" mice. Crossing these mice to Flk2-Cre mice results in "HSC-DTR" mice which exhibit HSC-selective DT sensitivity. We demonstrate robust, rapid, and highly selective cell ablation in these models. These new mouse models provide a platform to test whether HSCs are required for long-term hematopoiesis in vivo, for understanding the mechanisms regulating HSC engraftment, and interrogating in vivo hematopoietic differentiation pathways and mechanisms regulating hematopoietic homeostasis.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

A quantitative hematopoietic stem cell reconstitution protocol: Accounting for recipient variability, tissue distribution and cell half-lives.

Hematopoietic stem and progenitor cell (HSPC) transplantation is the paradigm for stem cell therapies. The protocol described here enables quantitative assessment of the body-wide HSPC reconstitution of different mature hematopoietic cells in mice based on their presence in circulating blood. The method determines donor-derived mature cell populations per mouse, over time, by quantitatively obtaining their absolute numbers in the peripheral blood and utilizing previously assessed tissue-distribution factors. A Markov-based birth/death computational model accounts for the drastic differences in mature cell half-lives. By quantifying the number of cells produced and eliminating host variability, the protocol can be used to directly compare the lineage output of different types of HSPCs on a per cell basis, thereby clarifying the lineage potential and expansion capacity of different cell populations. These protocols were developed for hematopoiesis, but can readily be extended to other contexts by simply replacing the cell types and distributions.

Clonal and Quantitative In Vivo Assessment of Hematopoietic Stem Cell Differentiation Reveals Strong Erythroid Potential of Multipotent Cells.

Hematopoiesis is arguably one of the best understood stem cell systems; however, significant challenges remain to reach a consensus understanding of the lineage potential, heterogeneity, and relationships of hematopoietic stem and progenitor cell populations. To gain new insights, we performed quantitative analyses of mature cell production from hematopoietic stem cells (HSCs) and multiple hematopoietic progenitor populations. Assessment of the absolute numbers of mature cell types produced by each progenitor cell revealed a striking erythroid dominance of all myeloid-competent progenitors assessed, accompanied by strong platelet reconstitution. All populations with myeloid potential also produced robust numbers of red blood cells and platelets in vivo. Clonal analysis by single-cell transplantation and by spleen colony assays revealed that a significant fraction of HSCs and multipotent progenitors have multilineage potential at the single-cell level. These new insights prompt an erythroid-focused model of hematopoietic differentiation.

Optimizing drug discovery by Investigative Toxicology: Current and future trends.

Investigative Toxicology describes the de-risking and mechanistic elucidation of toxicities, supporting early safety decisions in the pharmaceutical industry. Recently, Investigative Toxicology has contributed to a shift in pharmaceutical toxicology, from a descriptive to an evidence-based, mechanistic discipline. This was triggered by high costs and low throughput of Good Laboratory Practice in vivo studies, and increasing demands for adhering to the 3R (Replacement, Reduction and Refinement) principles of animal welfare. Outside the boundaries of regulatory toxicology, Investigative Toxicology has the flexibility to embrace new technologies, enhancing translational steps from in silico, in vitro to in vivo mechanistic understanding to eventually predict human response. One major goal of Investigative Toxicology is improving preclinical decisions, which coincides with the concept of animal-free safety testing. Currently, compounds under preclinical development are being discarded due to the use of inappropriate animal models. Progress in Investigative Toxicology could lead to humanized in vitro test systems and the development of medicines less reliant on animal tests. To advance this field a group of 14 European-based leaders from the pharmaceutical industry founded the Investigative Toxicology Leaders Forum (ITLF), an open, non-exclusive and pre-competitive group that shares knowledge and experience. The ITLF collaborated with the Centre for Alternatives to Animal Testing Europe (CAAT-Europe) to organize an "Investigative Toxicology Think-Tank", which aimed to enhance the interaction with experts from academia and regulatory bodies in the field. Summarizing the topics and discussion of the workshop, this article highlights Investigative Toxicology's position by identifying key challenges and perspectives.

In vivo manipulation of the extracellular matrix induces vascular regression in a basal chordate.

We investigated the physical role of the extracellular matrix (ECM) in vascular homeostasis in the basal chordate Botryllus schlosseri, which has a large, transparent, extracorporeal vascular network encompassing an area >100 cm2 We found that the collagen cross-linking enzyme lysyl oxidase is expressed in all vascular cells and that in vivo inhibition using ß-aminopropionitrile (BAPN) caused a rapid, global regression of the entire network, with some vessels regressing >10 mm within 16 h. BAPN treatment changed the ultrastructure of collagen fibers in the vessel basement membrane, and the kinetics of regression were dose dependent. Pharmacological inhibition of both focal adhesion kinase (FAK) and Raf also induced regression, and levels of phosphorylated FAK in vascular cells decreased during BAPN treatment and FAK inhibition but not Raf inhibition, suggesting that physical changes in the vessel ECM are detected via canonical integrin signaling pathways. Regression is driven by apoptosis and extrusion of cells through the basal lamina, which are then engulfed by blood-borne phagocytes. Extrusion and regression occurred in a coordinated manner that maintained vessel integrity, with no loss of barrier function. This suggests the presence of regulatory mechanisms linking physical changes to a homeostatic, tissue-level response.

Deconvoluting Kinase Inhibitor Induced Cardiotoxicity.

Many drugs designed to inhibit kinases have their clinical utility limited by cardiotoxicity-related label warnings or prescribing restrictions. While this liability is widely recognized, designing safer kinase inhibitors (KI) requires knowledge of the causative kinase(s). Efforts to unravel the kinases have encountered pharmacology with nearly prohibitive complexity. At therapeutically relevant concentrations, KIs show promiscuity distributed across the kinome. Here, to overcome this complexity, 65 KIs with known kinome-scale polypharmacology profiles were assessed for effects on cardiomyocyte (CM) beating. Changes in human iPSC-CM beat rate and amplitude were measured using label-free cellular impedance. Correlations between beat effects and kinase inhibition profiles were mined by computation analysis (Matthews Correlation Coefficient) to identify associated kinases. Thirty kinases met criteria of having (1) pharmacological inhibition correlated with CM beat changes, (2) expression in both human-induced pluripotent stem cell-derived cardiomyocytes and adult heart tissue, and (3) effects on CM beating following single gene knockdown. A subset of these 30 kinases were selected for mechanistic follow up. Examples of kinases regulating processes spanning the excitation-contraction cascade were identified, including calcium flux (RPS6KA3, IKBKE) and action potential duration (MAP4K2). Finally, a simple model was created to predict functional cardiotoxicity whereby inactivity at three sentinel kinases (RPS6KB1, FAK, STK35) showed exceptional accuracy in vitro and translated to clinical KI safety data. For drug discovery, identifying causative kinases and introducing a predictive model should transform the ability to design safer KI medicines. For cardiovascular biology, discovering kinases previously unrecognized as influencing cardiovascular biology should stimulate investigation of underappreciated signaling pathways.

Binary classification of imbalanced datasets using conformal prediction.

Aggregated Conformal Prediction is used as an effective alternative to other, more complicated and/or ambiguous methods involving various balancing measures when modelling severely imbalanced datasets. Additional explicit balancing measures other than those already apart of the Conformal Prediction framework are shown not to be required. The Aggregated Conformal Prediction procedure appears to be a promising approach for severely imbalanced datasets in order to retrieve a large majority of active minority class compounds while avoiding information loss or distortion.

Evidence for the hERG Liability of Antihistamines, Antipsychotics, and Anti-Infective Agents: A Systematic Literature Review From the ARITMO Project.

A systematic review was performed to categorize the hERG (human ether-a-go-go-related gene) liability of antihistamines, antipsychotics, and anti-infectives and to compare it with current clinical risk of torsade de pointes (TdP). Eligible studies were hERG assays reporting half-minimal inhibitory concentrations (IC50). A "hERG safety margin" was calculated from the IC50 divided by the peak human plasma concentration (free Cmax ). A margin below 30 defined hERG liability. Each drug was assigned an "uncertainty score" based on volume, consistency, precision, and internal and external validity of evidence. The hERG liability was compared to existing knowledge on TdP risk (www.credibledrugs.org). Of 1828 studies, 82 were eligible, allowing calculation of safety margins for 61 drugs. Thirty-one drugs (51%) had evidence of hERG liability including 6 with no previous mention of TdP risk (eg, desloratadine, lopinavir). Conversely, 16 drugs (26%) had no evidence of hERG liability including 6 with known, or at least conditional or possible, TdP risk (eg, chlorpromazine, sulpiride). The main sources of uncertainty were the validity of the experimental conditions used (antihistamines and antipsychotics) and nonuse of reference compounds (anti-infectives). In summary, hERG liability was categorized for 3 widely used drug classes, incorporating a qualitative assessment of the strength of available evidence. Some concordance with TdP risk was observed, although several drugs had hERG liability without evidence of clinical risk and vice versa. This may be due to gaps in clinical evidence, limitations of hERG/Cmax data, or other patient/drug-specific factors that contribute to real-life TdP risk.

A Transient Developmental Hematopoietic Stem Cell Gives Rise to Innate-like B and T Cells.

The generation of distinct hematopoietic cell types, including tissue-resident immune cells, distinguishes fetal from adult hematopoiesis. However, the mechanisms underlying differential cell production to generate a layered immune system during hematopoietic development are unclear. Using an irreversible lineage-tracing model, we identify a definitive hematopoietic stem cell (HSC) that supports long-term multilineage reconstitution upon transplantation into adult recipients but does not persist into adulthood in situ. These HSCs are fully multipotent, yet they display both higher lymphoid cell production and greater capacity to generate innate-like B and T lymphocytes as compared to coexisting fetal HSCs and adult HSCs. Thus, these developmentally restricted HSCs (drHSCs) define the origin and generation of early lymphoid cells that play essential roles in establishing self-recognition and tolerance, with important implications for understanding autoimmune disease, allergy, and rejection of transplanted organs.

Hematopoietic stem cell-specific GFP-expressing transgenic mice generated by genetic excision of a pan-hematopoietic reporter gene.

Selective labeling of specific cell types by expression of green fluorescent protein (GFP) within the hematopoietic system would have great utility in identifying, localizing, and tracking different cell populations in flow cytometry, microscopy, lineage tracing, and transplantation assays. In this report, we describe the generation and characterization of a new transgenic mouse line with specific GFP labeling of all nucleated hematopoietic cells and platelets. This new "Vav-GFP" mouse line labels the vast majority of hematopoietic cells with GFP during both embryonic development and adulthood, with particularly high expression in hematopoietic stem and progenitor cells (HSPCs). With the exception of transient labeling of fetal endothelial cells, GFP expression is highly selective for hematopoietic cells and persists in donor-derived progeny after transplantation of HSPCs. Finally, we also demonstrate that the loxP-flanked reporter allows for specific GFP labeling of different hematopoietic cell subsets when crossed to various Cre reporter lines. By crossing Vav-GFP mice to Flk2-Cre mice, we obtained robust and highly selective GFP expression in hematopoietic stem cells (HSCs). These data describe a new mouse model capable of directing GFP labeling exclusively of hematopoietic cells or exclusively of HSCs.

Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays.

Quantitative structure-activity relationships (QSAR) are critical to exploitation of the chemical information in toxicology databases. Exploitation can be extraction of chemical knowledge from the data but also making predictions of new chemicals based on quantitative analysis of past findings. In this study, we analyzed the ToxCast and Tox21 estrogen receptor data sets using Conformal Prediction to enhance the full exploitation of the information in these data sets. We applied aggregated conformal prediction (ACP) to the ToxCast and Tox21 estrogen receptor data sets using support vector machine classifiers to compare overall performance of the models but, more importantly, to explore the performance of ACP on data sets that are significantly enriched in one class without employing sampling strategies of the training set. ACP was also used to investigate the problem of applicability domain using both data sets. Comparison of ACP to previous results obtained on the same data sets using traditional QSAR approaches indicated similar overall balanced performance to methods in which careful training set selections were made, e.g., sensitivity and specificity for the external Tox21 data set of 70-75% and far superior results to those obtained using traditional methods without training set sampling where the corresponding results showed a clear imbalance of 50 and 96%, respectively. Application of conformal prediction to imbalanced data sets facilitates an unambiguous analysis of all data, allows accurate predictive models to be built which display similar accuracy in external validation to external validation, and, most importantly, allows an unambiguous treatment of the applicability domain.

Behavioural toxicity assessment of silver ions and nanoparticles on zebrafish using a locomotion profiling approach.

Zebrafish (Danio rerio) is not only a widely used species in the Fish Embryo Toxicity (FET) test but also an emerging model in behavioural ecotoxicology. By using automatic behaviour tracking technology, locomotion of developing zebrafish (ZF) larvae can be accurately recorded and potentially used in an ecotoxicological context to detect toxicant-induced behavioural alterations. In this study, we explored if and how quantitative locomotion data can be used for sub-lethal toxicity testing within the FET framework. We exposed ZF embryos to silver ions and nanoparticles, which previously have been reported to cause neurodevelopmental toxicity and behavioural retardation in early-life stages of ZF. Exposure to a broad range of silver (Ag(+) and AgNPs) concentrations was conducted, and developmental toxicity was assessed using FET criteria. For behavioural toxicity assessment, locomotion of exposed ZF eleutheroembryos (120hpf) was quantified according to a customised behavioural assay in an automatic video tracking system. A set of repeated episodes of dark/light stimulation were used to artificially stress ZF and evoke photo-motor responses, which were consequently utilized for locomotion profiling. Our locomotion-based behaviour profiling approach consisted of (1) dose-response ranking for multiple and single locomotion variables; (2) quantitative assessment of locomotion structure; and (3) analysis of ZF responsiveness to darkness stimulation. We documented that both silver forms caused adverse effects on development and inhibited hatchability and, most importantly, altered locomotion. High Ag(+) and AgNPs exposures significantly suppressed locomotion and a clear shift in locomotion towards inactivity was reported. Additionally, we noted that low, environmentally relevant Ag(+) concentrations may cause subordinate locomotive changes (hyperactivity) in developing fish. Overall, it was concluded that our locomotion-based behaviour-testing scheme can be used jointly with FET and can provide endpoints for sub-lethal toxicity. When combined with multivariate data analysis, this approach facilitated new insights for handling and analysis of data generated by automatized behavioural tracking systems.

Flagging Drugs That Inhibit the Bile Salt Export Pump.

The bile salt export pump (BSEP) is an ABC-transporter expressed at the canalicular membrane of hepatocytes. Its physiological role is to expel bile salts into the canaliculi from where they drain into the bile duct. Inhibition of this transporter may lead to intrahepatic cholestasis. Predictive computational models of BSEP inhibition may allow for fast identification of potentially harmful compounds in large databases. This article presents a predictive in silico model based on physicochemical descriptors that is able to flag compounds as potential BSEP inhibitors. This model was built using a training set of 670 compounds with available BSEP inhibition potencies. It successfully predicted BSEP inhibition for two independent test sets and was in a further step used for a virtual screening experiment. After in vitro testing of selected candidates, a marketed drug, bromocriptin, was identified for the first time as BSEP inhibitor. This demonstrates the usefulness of the model to identify new BSEP inhibitors and therefore potential cholestasis perpetrators.

Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination.

Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure-activity data where focus is on predictions with pre-defined confidence levels. A conformal predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.

Computational derivation of structural alerts from large toxicology data sets.

Structural alerts have been one of the backbones of computational toxicology and have applications in many areas including cosmetic, environmental, and pharmaceutical toxicology. The development of structural alerts has always involved a manual analysis of existing data related to a relevant end point followed by the determination of substructures that appear to be related to a specific outcome. The substructures are then analyzed for their utility in posterior validation studies, which at times have stretched over years or even decades. With higher throughput methods now being employed in many areas of toxicology, data sets are growing at an unprecedented rate. This growth has made manual analysis of data sets impractical in many cases. This report outlines a fully automatic method that highlights significant substructures for toxicologically important data sets. The method identifies important substructures by computationally breaking chemical structures into fragments and analyzing those fragments for their contribution to the given activity by the calculation of a p-value and a substructure accuracy. The method is intended to aid the expert in locating and analyzing alerts by automatic retrieval of alerts or by enhancing existing alerts. The method has been applied to a data set of AMES mutagenicity results and compared to the substructures generated by manual curation of this same data set as well as another computationally based substructure identification method. The results show that this method can retrieve significant substructures quickly, that the substructures are comparable and in some cases superior to those derived from manual curation, that the substructures found covers all previously known substructures, and that they can be used to make reasonably accurate predictions of AMES activity.

Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination.

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models.

The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package ( https://bitbucket.org/biolab/orange-reliability ) to the Orange data mining suite.