Functional Effects of Cardiomyocyte Injury in COVID-19.

COVID-19 affects multiple organs. Clinical data from the Mount Sinai Health System show that substantial numbers of COVID-19 patients without prior heart disease develop cardiac dysfunction. How COVID-19 patients develop cardiac disease is not known. We integrated cell biological and physiological analyses of human cardiomyocytes differentiated from human induced pluripotent stem cells (hiPSCs) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the presence of interleukins (ILs) with clinical findings related to laboratory values in COVID-19 patients to identify plausible mechanisms of cardiac disease in COVID-19 patients. We infected hiPSC-derived cardiomyocytes from healthy human subjects with SARS-CoV-2 in the absence and presence of IL-6 and IL-1ß. Infection resulted in increased numbers of multinucleated cells. Interleukin treatment and infection resulted in disorganization of myofibrils, extracellular release of troponin I, and reduced and erratic beating. Infection resulted in decreased expression of mRNA encoding key proteins of the cardiomyocyte contractile apparatus. Although interleukins did not increase the extent of infection, they increased the contractile dysfunction associated with viral infection of cardiomyocytes, resulting in cessation of beating. Clinical data from hospitalized patients from the Mount Sinai Health System show that a significant portion of COVID-19 patients without history of heart disease have elevated troponin and interleukin levels. A substantial subset of these patients showed reduced left ventricular function by echocardiography. Our laboratory observations, combined with the clinical data, indicate that direct effects on cardiomyocytes by interleukins and SARS-CoV-2 infection might underlie heart disease in COVID-19 patients. IMPORTANCE SARS-CoV-2 infects multiple organs, including the heart. Analyses of hospitalized patients show that a substantial number without prior indication of heart disease or comorbidities show significant injury to heart tissue, assessed by increased levels of troponin in blood. We studied the cell biological and physiological effects of virus infection of healthy human iPSC-derived cardiomyocytes in culture. Virus infection with interleukins disorganizes myofibrils, increases cell size and the numbers of multinucleated cells, and suppresses the expression of proteins of the contractile apparatus. Viral infection of cardiomyocytes in culture triggers release of troponin similar to elevation in levels of COVID-19 patients with heart disease. Viral infection in the presence of interleukins slows down and desynchronizes the beating of cardiomyocytes in culture. The cell-level physiological changes are similar to decreases in left ventricular ejection seen in imaging of patients' hearts. These observations suggest that direct injury to heart tissue by virus can be one underlying cause of heart disease in COVID-19.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens.

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

The derivation of a Reference Dose (RfD) for perfluorooctane sulfonate (PFOS) based on immune suppression.

Exposure to perfluorooctane sulfonate (PFOS) is ubiquitous in populations and environments worldwide. Its long half-life in humans, indefinite persistence in the environment, and awareness of its widespread presence in drinking water make the human health assessment of PFOS a priority. While developmental, endocrine, and hepatic effects, and increased serum cholesterol are among the outcomes resulting from PFOS exposure, immunosuppression has also consistently emerged as an adverse effect. An in-depth review of the relevant scientific literature on the toxicology of PFOS has identified immunosuppression as a sensitive endpoint for PFOS toxicity. Here, we focus specifically on that endpoint and provide a detailed derivation of a Reference Dose (RfD) of 1.8 × 10-6 mg/kg/day for chronic human exposure to PFOS. This RfD is based on decreased plaque-forming cell (PFC) response in mice, an endpoint that reflects suppression of the immune response to a foreign antigen. We additionally identify two endpoints in the epidemiology literature, decreased vaccine response and increased incidence of childhood infections, that are associated with PFOS exposure and that are consistent with and support the decreased PFC response endpoint from animal studies. We provide a weight of evidence analysis integrating the evidence from animal and epidemiology endpoints. Finally, we compare this RfD to the PFOS RfD derived by the United States Environmental Protection Agency (USEPA) Office of Water based on a developmental endpoint. Based on this comparison, and given our assessment, the USEPA RfD does not provide sufficient protection against the adverse health effects of PFOS. The RfD derived herein is intended to be public health protective and appropriately minimizes PFOS exposure based on available evidence.

Cell size assays for mass cytometry.

Mass cytometry offers the advantage of allowing the simultaneous measurement of a greater number parameters than conventional flow cytometry. However, to date, mass cytometry has lacked a reliable alternative to the light scatter properties that are commonly used as a cell size metric in flow cytometry (forward scatter intensity-FSC). Here, we report the development of two plasma membrane staining assays to evaluate mammalian cell size in mass cytometry experiments. One is based on wheat germ agglutinin (WGA) staining and the other on Osmium tetroxide (OsO4 ) staining, both of which have preferential affinity for cell membranes. We first perform imaging and flow cytometry experiments to establish a relationship between WGA staining intensity and traditional measures of cell size. We then incorporate WGA staining in mass cytometry analysis of human whole blood and show that WGA staining intensity has reproducible patterns within and across immune cell subsets that have distinct cell sizes. Lastly, we stain PBMCs or dissociated lung tissue with both WGA and OsO4 ; mass cytometry analysis demonstrates that the two staining intensities correlate well with one another. We conclude that both WGA and OsO4 may be used to acquire cell size-related parameters in mass cytometry experiments, and expect these stains to be broadly useful in expanding the range of parameters that can be measured in mass cytometry experiments. © 2016 International Society for Advancement of Cytometry.

Correction: Sparse sampling methods in multidimensional NMR.

Correction for 'Sparse sampling methods in multidimensional NMR' by Mehdi Mobli et al., Phys. Chem. Chem. Phys., 2012, 14, 10835-10843.

Nonuniform sampling and maximum entropy reconstruction in multidimensional NMR.

NMR spectroscopy is one of the most powerful and versatile analytic tools available to chemists. The discrete Fourier transform (DFT) played a seminal role in the development of modern NMR, including the multidimensional methods that are essential for characterizing complex biomolecules. However, it suffers from well-known limitations: chiefly the difficulty in obtaining high-resolution spectral estimates from short data records. Because the time required to perform an experiment is proportional to the number of data samples, this problem imposes a sampling burden for multidimensional NMR experiments. At high magnetic field, where spectral dispersion is greatest, the problem becomes particularly acute. Consequently multidimensional NMR experiments that rely on the DFT must either sacrifice resolution in order to be completed in reasonable time or use inordinate amounts of time to achieve the potential resolution afforded by high-field magnets. Maximum entropy (MaxEnt) reconstruction is a non-Fourier method of spectrum analysis that can provide high-resolution spectral estimates from short data records. It can also be used with nonuniformly sampled data sets. Since resolution is substantially determined by the largest evolution time sampled, nonuniform sampling enables high resolution while avoiding the need to uniformly sample at large numbers of evolution times. The Nyquist sampling theorem does not apply to nonuniformly sampled data, and artifacts that occur with the use of nonuniform sampling can be viewed as frequency-aliased signals. Strategies for suppressing nonuniform sampling artifacts include the careful design of the sampling scheme and special methods for computing the spectrum. Researchers now routinely report that they can complete an N-dimensional NMR experiment 3(N-1) times faster (a 3D experiment in one ninth of the time). As a result, high-resolution three- and four-dimensional experiments that were prohibitively time consuming are now practical. Conversely, tailored sampling in the indirect dimensions has led to improved sensitivity. Further advances in nonuniform sampling strategies could enable further reductions in sampling requirements for high resolution NMR spectra, and the combination of these strategies with robust non-Fourier methods of spectrum analysis (such as MaxEnt) represent a profound change in the way researchers conduct multidimensional experiments. The potential benefits will enable more advanced applications of multidimensional NMR spectroscopy to study biological macromolecules, metabolomics, natural products, dynamic systems, and other areas where resolution, sensitivity, or experiment time are limiting. Just as the development of multidimensional NMR methods presaged multidimensional methods in other areas of spectroscopy, we anticipate that nonuniform sampling approaches will find applications in other forms of spectroscopy.

A new approach to compressed sensing for NMR.

Compressed sensing (CS) has attracted a great deal of recent interest as an approach for spectrum analysis of nonuniformly sampled NMR data. Although theoretical justification for the method is abundant, it suffers from several weaknesses, among them poor convergence of some algorithms, and it remains an open question whether NMR spectra satisfy the sparsity requirements of CS theorems. The versions of CS used in NMR involve minimizing the l1 norm of the spectrum. They bear similarity to maximum entropy (MaxEnt) reconstruction, but critical comparison of the methods can be difficult. Here we describe a formalism that places CS and MaxEnt reconstruction on equal footing, enabling critical comparison of the two methods. We also describe a new algorithm for CS that restricts the computation of the l1 norm to the real channel for complex spectra and ensures causality. Preliminary 1D results demonstrate that this approach ameliorates some artifacts that can occur when using the l1 norm of the complex spectrum.

Hazard identification of the potential for dieldrin carcinogenicity to humans.

Although dieldrin׳s use in the U.S. was partially banned in the 1970s and its use was completely eliminated in 1987, dieldrin continues to be a common contaminant at hazardous waste sites. The USEPA׳s current cancer potency estimate for dieldrin was derived in 1987 and is based on the production of mouse liver tumors. Because of its environmental persistence and its relatively high USEPA cancer potency estimate, dieldrin functions as a cleanup "driver" in many hazardous site remediations. Since 1987, new risk assessment perspectives and new data on dieldrin׳s carcinogenic potential have arisen. This review presents a reassessment of dielrin׳s human cancer potential in light of these new data and new perspectives. Based on this reassessment, dieldrin may be carcinogenic through multiple modes of action. These modes of action may operate within the same tissue, or may be specific to individual tissues. Of the several possible carcinogenic modes of action for dieldrin, one or more may be more relevant to human cancer risk than others, but the relative importance of each is unknown. In addition, neither the details of the possible modes of action, nor the shape of the tumor dose-response curves associated with each are sufficiently well known to permit quantitative cancer dose-response modeling. Thus, the mouse liver tumor data used by the USEPA in its 1987 assessment remain the only quantitative data available for cancer dose-response modeling.

Random phase detection in multidimensional NMR.

Despite advances in resolution accompanying the development of high-field superconducting magnets, biomolecular applications of NMR require multiple dimensions in order to resolve individual resonances, and the achievable resolution is typically limited by practical constraints on measuring time. In addition to the need for measuring long evolution times to obtain high resolution, the need to distinguish the sign of the frequency constrains the ability to shorten measuring times. Sign discrimination is typically accomplished by sampling the signal with two different receiver phases or by selecting a reference frequency outside the range of frequencies spanned by the signal and then sampling at a higher rate. In the parametrically sampled (indirect) time dimensions of multidimensional NMR experiments, either method imposes an additional factor of 2 sampling burden for each dimension. We demonstrate that by using a single detector phase at each time sample point, but randomly altering the phase for different points, the sign ambiguity that attends fixed single-phase detection is resolved. Random phase detection enables a reduction in experiment time by a factor of 2 for each indirect dimension, amounting to a factor of 8 for a four-dimensional experiment, albeit at the cost of introducing sampling artifacts. Alternatively, for fixed measuring time, random phase detection can be used to double resolution in each indirect dimension. Random phase detection is complementary to nonuniform sampling methods, and their combination offers the potential for additional benefits. In addition to applications in biomolecular NMR, random phase detection could be useful in magnetic resonance imaging and other signal processing contexts.

Upper washita river experimental watersheds: land cover data sets (1974-2007) for two southwestern oklahoma agricultural watersheds.

Land cover data sets were developed for 1997, 1999, 2003, 2005, 2006, and 2007 for the Little Washita River and Fort Cobb Reservoir experimental watersheds (LWREW and FCREW, respectively), located in southwestern Oklahoma, to support remote sensing based studies of soil water content. A previously unpublished retrospective land cover analysis covering the years 1974, 1981, 1985, 1989, and 1994 was conducted to complement these data sets to gain a sense of the dynamics of land cover in both the LWREW and FCREW over the 33 yr. Each of these studies used satellite-based sensors of various spatial, radiometric, and spectral resolutions, but the number of images used, image date, and methods used to analyze the images varied from study to study. Our purpose was to document the details of the retrospective land cover study, to compare land cover between watersheds with time, and to compare findings from the various studies to elucidate changes or trends in land cover in each watershed during the 33 yr the data sets represent. Information on how to access to the data sets is also given. The LWREW was a grassland watershed that changed little during the study period. The FCREW was divided between grassland and cropland, but the cropland portion exhibited dynamic behavior that appeared correlated with peanut ( L.) price supports and Conservation Reserve programs. Dynamic land use information coupled with information concerning conservation practices will enhance assessment of conservation practice effectiveness as well as improve modeling of the fate and transport of chemicals and nutrients in watersheds.

Open-Source System for Real-Time Functional Assessment of In Vitro Filtration Barriers.

The integrity of the barrier between blood and the selective filtrate of solutes is important for homeostasis and its disruption contributes to many diseases. Microphysiological systems that incorporate synthetic or natural membranes with human cells can mimic biological filtration barriers, such as the glomerular filtration barrier in the kidney, and they can readily be used to study cellular filtration processes as well as drug effects and interactions. We present an affordable, open-source platform for the real-time monitoring of functional filtration status in engineered microphysiological systems. Using readily available components, our assay can linearly detect real-time concentrations of two target molecules, FITC-labeled inulin and Texas Red-labeled human-serum albumin, within clinically relevant ranges, and it can be easily modified for different target molecules of varying sizes and tags. We demonstrate the platform's ability to determine the concentration of our target molecules automatically and consistently. We show through an acellular context that the platform enables real-time tracking of size-dependent diffusion with minimal fluid volume loss and without manual extraction of media, making it suitable for continuous operational monitoring of filtration status in microphysiological system applications. The platform's affordability and integrability with microphysiological systems make it ideal for many precision medicine applications, including evaluation of drug nephrotoxicity and other forms of drug discovery.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Single-file diffusion of flagellin in flagellar filaments.

A bacterial flagellar filament is a cylindrical crystal of a protein known as flagellin. Flagellin subunits travel from the cytoplasm through a 2 nm axial pore and polymerize at the filament's distal end. They are supplied by a pump in the cell membrane powered by a proton-motive force. In a recent experiment, it was observed that growth proceeded at a rate of approximately one subunit every 2 s. Here, we asked whether transport of subunits through the pore at this rate could be effected by single-file diffusion, which we simulated by a random walk on a one-dimensional lattice. Assuming that the subunits are α-helical, the answer is yes, by a comfortable margin.

Growth of flagellar filaments of Escherichia coli is independent of filament length.

Bacterial flagellar filaments grow at their distal ends, from flagellin that travels through a central channel ∼2 nm in diameter. The flagellin is extruded from the cytoplasm by a pump powered by a proton motive force (PMF). We measured filament growth in cells near the mid-exponential-phase with flagellin bearing a specific cysteine-for-serine substitution, allowing filaments to be labeled with sulfhydryl-specific fluorescent dyes. We labeled filaments first with a green maleimide dye and then, following an additional period of growth, with a red maleimide dye. The contour lengths of the green and red segments were measured. The average lengths of red segments (∼2.3 µm) were the same regardless of the lengths of the green segments from which they grew (ranging from less than 1 to more than 9 µm in length). Thus, flagellar filaments do not grow at a rate that decreases exponentially with length, as formerly supposed. If flagellar filaments were broken by viscous shear, the broken filaments continued to grow. Identical results were obtained whether flagellin was expressed from fliC on the chromosome under the control of its native promoter or on a plasmid under the control of the arabinose promoter.

Data sampling in multidimensional NMR: fundamentals and strategies.

Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (free induction decay) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform, the intervals are kept uniform, and the Nyquist theorem dictates the largest value of the interval sufficient to avoid aliasing. With the proposal by Jeener of parametric sampling along an indirect time dimension, extension to multidimensional experiments employed the same sampling techniques used in one dimension, similarly subject to the Nyquist condition and suitable for processing via the discrete Fourier transform. The challenges of obtaining high-resolution spectral estimates from short data records were already well understood, and despite techniques such as linear prediction extrapolation, the achievable resolution in the indirect dimensions is limited by practical constraints on measuring time. The advent of methods of spectrum analysis capable of processing nonuniformly sampled data has led to an explosion in the development of novel sampling strategies that avoid the limits on resolution and measurement time imposed by uniform sampling. In this chapter we review the fundamentals of uniform and nonuniform sampling methods in one- and multidimensional NMR.

Sparse sampling methods in multidimensional NMR.

Although the discrete Fourier transform played an enabling role in the development of modern NMR spectroscopy, it suffers from a well-known difficulty providing high-resolution spectra from short data records. In multidimensional NMR experiments, so-called indirect time dimensions are sampled parametrically, with each instance of evolution times along the indirect dimensions sampled via separate one-dimensional experiments. The time required to conduct multidimensional experiments is directly proportional to the number of indirect evolution times sampled. Despite remarkable advances in resolution with increasing magnetic field strength, multiple dimensions remain essential for resolving individual resonances in NMR spectra of biological macromolecues. Conventional Fourier-based methods of spectrum analysis limit the resolution that can be practically achieved in the indirect dimensions. Nonuniform or sparse data collection strategies, together with suitable non-Fourier methods of spectrum analysis, enable high-resolution multidimensional spectra to be obtained. Although some of these approaches were first employed in NMR more than two decades ago, it is only relatively recently that they have been widely adopted. Here we describe the current practice of sparse sampling methods and prospects for further development of the approach to improve resolution and sensitivity and shorten experiment time in multidimensional NMR. While sparse sampling is particularly promising for multidimensional NMR, the basic principles could apply to other forms of multidimensional spectroscopy.

Chronologically matched toenail-Hg to hair-Hg ratio: temporal analysis within the Japanese community (U.S.).

BACKGROUND: Toenail-Hg levels are being used as a marker of methylmercury (MeHg) exposure in efforts to associate exposure with effects such as cardiovascular disease. There is a need to correlate this marker with more established biomarkers that presently underlie existing dose-response relationships in order to compare these relationships across studies. METHODS: As part of the Arsenic Mercury Intake Biometric Study, toenail clippings were collected at three time points over a period of one year amongst females from within the population of Japanese living near Puget Sound in Washington State (US). Variability in temporal intra-individual toenail-Hg levels was examined and chronologically matched hair and toenail samples were compared to more accurately define the toxicokinetic variability of Hg levels observed between the two compartments. RESULTS: Mean toenail-Hg values (n=43) for the 1st, 2nd and 3rd visits were 0.60, 0.60 and 0.56 ng/mg. Correlations were as follows: r=0.92 between 1st and 2nd clinic visits, r=0.75 between 1st and 3rd visits and r=0.87 between 2nd and 3rd visits. With few exceptions, toenail-Hg values from any visit were within 50-150% of the individual's mean toenail-Hg level. Nearly all participants had less than a two-fold change in toenail-Hg levels across the study period. A regression model of the relationship between toenail-Hg and hair-Hg (n = 41) levels representing the same time period of exposure, gave a slope (Hg ng/mg) of 2.79 for hair relative to toenail (r=0.954). CONCLUSIONS: A chronologically matched hair-Hg to toenail-Hg ratio has been identified within a population that consumes fish regularly and in quantity. Intra-individual variation in toenail-Hg levels was less than two-fold and may represent dietary-based fluctuations in body burden for individuals consuming various fish species with different contaminant levels. The chronologically matched ratio will be useful for relating MeHg exposure and dose-response derived from toenail-Hg measurements to those derived from hair-Hg measurements in other studies, and may be useful in future investigations as an indicator of stable MeHg body burden within a population.

Single blood-Hg samples can result in exposure misclassification: temporal monitoring within the Japanese community (United States).

BACKGROUND: The most prominent non-occupational source of exposure to methylmercury is the consumption of fish. In this study we examine a fish consuming population to determine the extent of temporal exposure and investigate the extent to which single time estimates of methylmercury exposure based on blood-Hg concentration can provide reliable estimates of longer-term average exposure. METHODS: Blood-mercury levels were obtained from a portion of the Arsenic Mercury Intake Biometric Study (AMIBS) cohort. Specifically, 56 Japanese women residing in the Puget Sound area of Washington State, US were sampled on three occasions across a one-year period. RESULTS: An average of 135 days separated samples, with mean blood-mercury levels for the visits being 5.1, 6.6 and 5.0 µg/l and geometric means being 2.7, 4.5 and 3.1 µg/l. The blood-mercury levels in this group exceed national averages with geometric means for two of the visits being between the 90th and 95th percentiles of nationally observed levels and the lowest geometric mean being between the 75th and 90th percentile. Group means were not significantly different across sampling periods suggesting that exposure of combined subjects remained relatively constant. Comparing intra-individual results over time did not reveal a strong correlation among visits (r = 0.19, 0.50, 0.63 between 1st and 2nd, 2nd and 3rd, and 1st and 3rd sample results, respectively). In comparing blood-mercury levels across two sampling interval combinations (1st and 2nd, 2nd and 3rd, and 1st and 3rd visits, respectively), 58% (n = 34), 53% (n = 31) and 29% (n = 17) of the individuals had at least a 100% difference in blood-Hg levels. CONCLUSIONS: Point estimates of blood-mercury, when compared with three sample averages, may not reflect temporal variability and individual exposures estimated on the basis of single blood samples should be treated with caution as indicators of long-term exposure. Reliance on single blood samples can make predicting ongoing methylmercury exposure highly speculative due to the large intra-individual variability.