microRNAs signatures as potential biomarkers of structural cardiotoxicity in human-induced pluripotent stem-cell derived cardiomyocytes.

Identification of early biomarkers of heart injury and drug-induced cardiotoxicity is important to eliminate harmful drug candidates early in preclinical development and to prevent severe drug effects. The main objective of this study was to investigate the expression of microRNAs (miRNAs) in human-induced pluripotent stem cell cardiomyocytes (hiPSC-CM) in response to a broad range of cardiotoxic drugs. Next generation sequencing was applied to hiPSC-CM treated for 72 h with 40 drugs falling into the categories of functional (i.e., ion channel blockers), structural (changes in cardiomyocytes structure), and general (causing both functional and structural) cardiotoxicants as well as non-cardiotoxic drugs. The largest changes in miRNAs expression were observed after treatments with structural or general cardiotoxicants. The number of deregulated miRNAs was the highest for idarubicin, mitoxantrone, and bortezomib treatments. RT-qPCR validation confirmed upregulation of several miRNAs across multiple treatments at therapeutically relevant concentrations: hsa-miR-187-3p, hsa-miR-146b-5p, hsa-miR-182-5p (anthracyclines); hsa-miR-365a-5p, hsa-miR-185-3p, hsa-miR-184, hsa-miR-182-5p (kinase inhibitors); hsa-miR-182-5p, hsa-miR-126-3p and hsa-miR-96-5p (common some anthracyclines, kinase inhibitors and bortezomib). Further investigations showed that an upregulation of hsa-miR-187-3p and hsa-miR-182-5p could serve as a potential biomarker of structural cardiotoxicity and/or an additional endpoint to characterize cardiac injury in vitro.

Robust inertial sensing with point-source atom interferometry for interferograms spanning a partial period.

Point source atom interferometry (PSI) uses the velocity distribution in a cold atom cloud to simultaneously measure one axis of acceleration and two axes of rotation from the spatial distribution of interferometer phase in an expanded cloud of atoms. Previously, the interferometer phase has been found from the phase, orientation, and period of the resulting spatial atomic interference fringe images. For practical applications in inertial sensing and precision measurement, it is important to be able to measure a wide range of system rotation rates, corresponding to interferograms with far less than one full interference fringe to very many fringes. Interferogram analysis techniques based on image processing used previously for PSI are challenging to implement for low rotation rates that generate less than one full interference fringe across the cloud. We introduce a new experimental method that is closely related to optical phase-shifting interferometry that is effective in extracting rotation values from signals consisting of fractional fringes as well as many fringes without prior knowledge of the rotation rate. The method finds the interferometer phase for each pixel in the image from four interferograms, each with a controlled Raman laser phase shift, to reconstruct the underlying atomic interferometer phase map without image processing.

Active stabilization of alkali-atom vapor density with a solid-state electrochemical alkali-atom source.

We report a demonstration of vapor-phase Rubidium (Rb) density stabilization in a vapor cell using a solid-state electrochemical Rb source device. Clear Rb density stabilization is observed. Further demonstrations show that the temperature coefficient for Rb density can be reduced more than 100 times when locked and the device's power consumption is less than 10 mW. Preliminary investigation of the locking dynamic range shows that the Rb density is well stabilized when the initial density is five times higher (33 × 109 /cm3) than the set point density (6 × 109 /cm3). Active stabilization with this device is of high interest for portable cold-atom microsystems where large ambient temperature working ranges and low power consumption are required.

Low helium permeation cells for atomic microsystems technology.

Laser spectroscopy of atoms confined in vapor cells can be strongly affected by the presence of background gases. A significant source of vacuum contamination is the permeation of gases such as helium (He) through the walls of the cell. Aluminosilicate glass (ASG) is a material with a helium permeation rate that is many orders of magnitude lower than borosilicate glass, which is commonly used for cell fabrication. We have identified a suitable source of ASG that is fabricated in wafer form and can be anodically bonded to silicon. We have fabricated chip-scale alkali vapor cells using this glass for the windows and we have measured the helium permeation rate using the pressure shift of the hyperfine clock transition. We demonstrate micro fabricated cells with He permeation rates at least three orders of magnitude lower than that of cells made with borosilicate glass at room temperature. Such cells may be useful in compact vapor-cell atomic clocks and as a micro fabricated platform suitable for the generation of cold atom samples.

Atom number in magneto-optic traps with millimeter scale laser beams.

We measure the number of atoms N trapped in a conventional vapor-cell magneto-optic trap (MOT) using beams that have a diameter d in the range 1-5 mm. We show that the N is proportional to d(3.6) scaling law observed for larger MOTs is a robust approximation for optimized MOTs with beam diameters as small as 3 mm. For smaller beams, the description of the scaling depends on how d is defined. The most consistent picture of the scaling is obtained when d is defined as the diameter where the intensity profile of the trapping beams decreases to the saturation intensity. Using this definition, N scales as d(6) for d<2.3 mm but, at larger d, N still scales as d(3.6).

Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening.

A metabolic biomarker-based in vitro assay utilizing human embryonic stem (hES) cells was developed to identify the concentration of test compounds that perturbs cellular metabolism in a manner indicative of teratogenicity. This assay is designed to aid the early discovery-phase detection of potential human developmental toxicants. In this study, metabolomic data from hES cell culture media were used to assess potential biomarkers for development of a rapid in vitro teratogenicity assay. hES cells were treated with pharmaceuticals of known human teratogenicity at a concentration equivalent to their published human peak therapeutic plasma concentration. Two metabolite biomarkers (ornithine and cystine) were identified as indicators of developmental toxicity. A targeted exposure-based biomarker assay using these metabolites, along with a cytotoxicity endpoint, was then developed using a 9-point dose-response curve. The predictivity of the new assay was evaluated using a separate set of test compounds. To illustrate how the assay could be applied to compounds of unknown potential for developmental toxicity, an additional 10 compounds were evaluated that do not have data on human exposure during pregnancy, but have shown positive results in animal developmental toxicity studies. The new assay identified the potential developmental toxicants in the test set with 77% accuracy (57% sensitivity, 100% specificity). The assay had a high concordance (≥75%) with existing in vivo models, demonstrating that the new assay can predict the developmental toxicity potential of new compounds as part of discovery phase testing and provide a signal as to the likely outcome of required in vivo tests.

Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics.

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case-control study of 499 autistic and 209 typically developing (TYP) children, ages 18-48 months, enrolled in the Children's Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5-11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants' biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues.

BACKGROUND: The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOXqP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOXqP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues. METHODS: VPA and a series of structural analogues were tested with the devTOXqP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses. RESULTS: The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data. CONCLUSIONS: This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOXqP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics.

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

A Targeted Metabolomics-Based Assay Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Identifies Structural and Functional Cardiotoxicity Potential.

Implementing screening assays that identify functional and structural cardiotoxicity earlier in the drug development pipeline has the potential to improve safety and decrease the cost and time required to bring new drugs to market. In this study, a metabolic biomarker-based assay was developed that predicts the cardiotoxicity potential of a drug based on changes in the metabolism and viability of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). Assay development and testing was conducted in 2 phases: (1) biomarker identification and (2) targeted assay development. In the first phase, metabolomic data from hiPSC-CM spent media following exposure to 66 drugs were used to identify biomarkers that identified both functional and structural cardiotoxicants. Four metabolites that represent different metabolic pathways (arachidonic acid, lactic acid, 2'-deoxycytidine, and thymidine) were identified as indicators of cardiotoxicity. In phase 2, a targeted, exposure-based biomarker assay was developed that measured these metabolites and hiPSC-CM viability across an 8-point concentration curve. Metabolite-specific predictive thresholds for identifying the cardiotoxicity potential of a drug were established and optimized for balanced accuracy or sensitivity. When predictive thresholds were optimized for balanced accuracy, the assay predicted the cardiotoxicity potential of 81 drugs with 86% balanced accuracy, 83% sensitivity, and 90% specificity. Alternatively, optimizing the thresholds for sensitivity yields a balanced accuracy of 85%, 90% sensitivity, and 79% specificity. This new hiPSC-CM-based assay provides a paradigm that can identify structural and functional cardiotoxic drugs that could be used in conjunction with other endpoints to provide a more comprehensive evaluation of a drug's cardiotoxicity potential.

A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project.

Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty-four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4-hydroxyproline, or α-ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%-57%) and 91% specificity (95% CI, 86%-94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments. LAY SUMMARY: Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood-based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270-1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children. METHODS: Dysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation. RESULTS: We identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort. CONCLUSIONS: Identification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions.

IT disaster recovery: are you prepared?

New Web technologies provide new opportunities but also include new risks. In an article in the May 2003 edition of Harvard Business Review, Editor Nicolas G. Carr said, "executives need to shift their attention from IT opportunities to IT risks-from offense to defense." That's probably a bit extreme. A better approach is to look at IT the same way you look at any business proposition. Every decision should be an informed decision. You should weigh the opportunities against the risks in order to select the best option. Then, once you have made your decision, take the necessary steps to minimize and prepare for the risks. This includes preparing for whatever disaster may come your way.

A human induced pluripotent stem cell-based in vitro assay predicts developmental toxicity through a retinoic acid receptor-mediated pathway for a series of related retinoid analogues.

The relative developmental toxicity potency of a series of retinoid analogues was evaluated using a human induced pluripotent stem (iPS) cell assay that measures changes in the biomarkers ornithine and cystine. Analogue potency was predicted, based on the assay endpoint of the ornithine/cystine (o/c) ratio, to be all-trans-retinoic acid>TTNPB>13-cis-retinoic acid≈9-cis-retinoic acid>acitretin>etretinate>retinol. These rankings correlate with in vivo data and demonstrate successful application of the assay to rank a series of related toxic and non-toxic compounds. The retinoic acid receptor α (RARα)-selective antagonist Ro 41-5253 inhibited the cystine perturbation caused by all-trans-retinoic acid, TTNPB, 13-cis-retinoic acid, 9-cis-retinoic acid, and acitretin. Ornithine was altered independent of RARα in all retinoids except acitretin. These results suggest a role for an RARα-mediated mechanism in retinoid-induced developmental toxicity through altered cystine metabolism.

An optimized microfabricated platform for the optical generation and detection of hyperpolarized 129Xe.

Low thermal-equilibrium nuclear spin polarizations and the need for sophisticated instrumentation render conventional nuclear magnetic resonance (NMR) spectroscopy and imaging (MRI) incompatible with small-scale microfluidic devices. Hyperpolarized 129Xe gas has found use in the study of many materials but has required very large and expensive instrumentation. Recently a microfabricated device with modest instrumentation demonstrated all-optical hyperpolarization and detection of 129Xe gas. This device was limited by 129Xe polarizations less than 1%, 129Xe NMR signals smaller than 20 nT, and transport of hyperpolarized 129Xe over millimeter lengths. Higher polarizations, versatile detection schemes, and flow of 129Xe over larger distances are desirable for wider applications. Here we demonstrate an ultra-sensitive microfabricated platform that achieves 129Xe polarizations reaching 7%, NMR signals exceeding 1 µT, lifetimes up to 6 s, and simultaneous two-mode detection, consisting of a high-sensitivity in situ channel with signal-to-noise of 105 and a lower-sensitivity ex situ detection channel which may be useful in a wider variety of conditions. 129Xe is hyperpolarized and detected in locations more than 1 cm apart. Our versatile device is an optimal platform for microfluidic magnetic resonance in particular, but equally attractive for wider nuclear spin applications benefitting from ultra-sensitive detection, long coherences, and simple instrumentation.

Supporting read-across using biological data.

Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

Optical hyperpolarization and NMR detection of 129Xe on a microfluidic chip.

Optically hyperpolarized (129)Xe gas has become a powerful contrast agent in nuclear magnetic resonance (NMR) spectroscopy and imaging, with applications ranging from studies of the human lung to the targeted detection of biomolecules. Equally attractive is its potential use to enhance the sensitivity of microfluidic NMR experiments, in which small sample volumes yield poor sensitivity. Unfortunately, most (129)Xe polarization systems are large and non-portable. Here we present a microfabricated chip that optically polarizes (129)Xe gas. We have achieved (129)Xe polarizations >0.5% at flow rates of several microlitres per second, compatible with typical microfluidic applications. We employ in situ optical magnetometry to sensitively detect and characterize the (129)Xe polarization at magnetic fields of 1 µT. We construct the device using standard microfabrication techniques, which will facilitate its integration with existing microfluidic platforms. This device may enable the implementation of highly sensitive (129)Xe NMR in compact, low-cost, portable devices.

Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.

BACKGROUND: The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. OBJECTIVES: To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. METHODS: Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. RESULTS: A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. CONCLUSIONS: This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.