Integration of lipidomics and transcriptomics data towards a systems biology model of sphingolipid metabolism

<p>Abstract</p> <p>Background</p> <p>Sphingolipids play important roles in cell structure and function as well as in the pathophysiology of many diseases. Many of the intermediates of sphingolipid biosynthesis are highly bioactive and sometimes have antagonistic activities, for example, ceramide promotes apoptosis whereas sphingosine-1-phosphate can inhibit apoptosis and induce cell growth; therefore, quantification of the metabolites and modeling of the sphingolipid network is imperative for an understanding of sphingolipid biology.</p> <p>Results</p> <p>In this direction, the LIPID MAPS Consortium is developing methods to quantitate the sphingolipid metabolites in mammalian cells and is investigating their application to studies of the activation of the RAW264.7 macrophage cell by a chemically defined endotoxin, Kdo₂-Lipid A. Herein, we describe a model for the C₁₆-branch of sphingolipid metabolism (i.e., for ceramides with palmitate as the N-acyl-linked fatty acid, which is selected because it is a major subspecies for all categories of complex sphingolipids in RAW264.7 cells) integrating lipidomics and transcriptomics data and using a two-step matrix-based approach to estimate the rate constants from experimental data. The rate constants obtained from the first step are further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data for all species. The robustness of the model is validated through parametric sensitivity analysis.</p> <p>Conclusions</p> <p>A quantitative model of the sphigolipid pathway is developed by integrating metabolomics and transcriptomics data with legacy knowledge. The model could be used to design experimental studies of how genetic and pharmacological perturbations alter the flux through this important lipid biosynthetic pathway.</p

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits

Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.Comment: Accepted for presentation at the FIRST WORKSHOP ON COMPUTATIONAL & AFFECTIVE INTELLIGENCE IN HEALTHCARE APPLICATIONS (VULNERABLE POPULATIONS) In Conjunction with the International Conference on Pattern Recognition (ICPR) 202

Remote Sensing Reflectance and Inherent Optical Properties in the Mid-mesohaline Chesapeake Bay

We used an extensive set of bio-optical data and radiative transfer (RT) model simulations of radiation fields to investigate relationships between inherent optical properties and remotely sensed quantities in the optically complex, mid-mesohaline Chesapeake Bay waters. Field observations showed that the chlorophyll algorithms used by the MODIS (MODerate resolution Imaging Spectroradiometer) ocean color sensor (i.e. Chlor_a, chlor_MODIS, chlor_a_3 products) do not perform accurately in these Case 2 waters. This is because, when applied to waters with high concentrations of chlorophyll, all MODIS algorithms are based on empirical relationships between chlorophyll concentration and blue-green wavelength remote sensing reflectance (Rrs) ratios that do not account for the typically strong blue-wavelength absorption by non-covarying, dissolved and non-algal particulate components. Stronger correlation was observed between chlorophyll concentration and Rrs ratios in the red (i.e. Rrs(677)/Rrs(554)) where dissolved and non-algal particulate absorption become exponentially smaller. Regionally-specific algorithms that are based on the phytoplankton optical properties in the red wavelength region provide a better basis for satellite monitoring of phytoplankton blooms in these Case 2 waters. Good optical closure was obtained between independently measured Rrs spectra and the optical properties of backscattering, b(sub b), and absorption, a, over the wide range of in-water conditions observed in the Chesapeake Bay. Observed variability in the quantity f/Q (proportionality factor in the relationship between Rrs and the water inherent optical properties ratio b(sub b)/(a+b(sub b)) was consistent with RT model calculations for the specific measurement geometry and water bio-optical characteristics. Data and model results showed that f/Q values in these Case 2 coastal waters are not considerably different from those estimated in previous studies for Case 1 waters. Variation in surface backscattering significantly affected Rrs magnitude across the visible spectrum and was most strongly correlated (R(sup 2)=0.88) with observed variability in Rrs at 670 nm. Surface values of particulate backscattering were strongly correlated with non-algal particulate absorption, a(sub nap), in the blue wavelengths (R(sup 2)=0.83). These results, along with the measured values of backscattering fraction magnitude and non-algal particulate absorption spectral slope, suggest that suspended non-algal particles with high inorganic content are the major water constituents regulating b(sub b) variability in the mid-mesohaline Chesapeake Bay. Remote retrieval of surface b(sub b) and (a(sub nap), from Rrs(670) can be used in regionally-specific satellite algorithms to separate contribution by non-algal particles and dissolved organic matter to total light absorption in the blue, and monitor non-algal suspended particle concentration and distribution in these Case 2 waters

Bio-Optics of the Chesapeake Bay from Measurements and Radiative Transfer Calculations

We combined detailed bio-optical measurements and radiative transfer (RT) modeling to perform an optical closure experiment for optically complex and biologically productive Chesapeake Bay waters. We used this experiment to evaluate certain assumptions commonly used when modeling bio-optical processes, and to investigate the relative importance of several optical characteristics needed to accurately model and interpret remote sensing ocean-color observations in these Case 2 waters. Direct measurements were made of the magnitude, variability, and spectral characteristics of backscattering and absorption that are critical for accurate parameterizations in satellite bio-optical algorithms and underwater RT simulations. We found that the ratio of backscattering to total scattering in the mid-mesohaline Chesapeake Bay varied considerably depending on particulate loading, distance from land, and mixing processes, and had an average value of 0.0128 at 530 nm. Incorporating information on the magnitude, variability, and spectral characteristics of particulate backscattering into the RT model, rather than using a volume scattering function commonly assumed for turbid waters, was critical to obtaining agreement between RT calculations and measured radiometric quantities. In situ measurements of absorption coefficients need to be corrected for systematic overestimation due to scattering errors, and this correction commonly employs the assumption that absorption by particulate matter at near infrared wavelengths is zero

TIEG1/KLF10 Modulates Runx2 Expression and Activity in Osteoblasts

Deletion of TIEG1/KLF10 in mice results in a gender specific osteopenic skeletal phenotype with significant defects in both cortical and trabecular bone, which are observed only in female animals. Calvarial osteoblasts isolated from TIEG1 knockout (KO) mice display reduced expression levels of multiple bone related genes, including Runx2, and exhibit significant delays in their mineralization rates relative to wildtype controls. These data suggest that TIEG1 plays an important role in regulating Runx2 expression in bone and that decreased Runx2 expression in TIEG1 KO mice is in part responsible for the observed osteopenic phenotype. In this manuscript, data is presented demonstrating that over-expression of TIEG1 results in increased expression of Runx2 while repression of TIEG1 results in suppression of Runx2. Transient transfection and chromatin immunoprecipitation assays reveal that TIEG1 directly binds to and activates the Runx2 promoter. The zinc finger containing domain of TIEG1 is necessary for this regulation supporting that activation occurs through direct DNA binding. A role for the ubiquitin/proteasome pathway in fine tuning the regulation of Runx2 expression by TIEG1 is also implicated in this study. Additionally, the regulation of Runx2 expression by cytokines such as TGFβ1 and BMP2 is shown to be inhibited in the absence of TIEG1. Co-immunoprecipitation and co-localization assays indicate that TIEG1 protein associates with Runx2 protein resulting in co-activation of Runx2 transcriptional activity. Lastly, Runx2 adenoviral infection of TIEG1 KO calvarial osteoblasts leads to increased expression of Runx2 and enhancement of their ability to differentiate and mineralize in culture. Taken together, these data implicate an important role for TIEG1 in regulating the expression and activity of Runx2 in osteoblasts and suggest that decreased expression of Runx2 in TIEG1 KO mice contributes to the observed osteopenic bone phenotype

The Parmodulin NRD-21 is an Allosteric Inhibitor of PAR1 Gq Signaling with Improved Anti-Inflammatory Activity and Stability

Novel analogs of the allosteric, biased PAR1 ligand ML161 (parmodulin 2, PM2) were prepared in order to identify potential anti-thrombotic and anti-inflammatory compounds of the parmodulin class with improved properties. Investigations of structure-activity relationships of the western portion of the 1,3-diaminobenzene scaffold were performed using an intracellular calcium mobilization assay with endothelial cells, and several heterocycles were identified that inhibited PAR1 at sub-micromolar concentrations. The oxazole NRD-21 was profiled in additional detail, and it was confirmed to act as a selective, reversible, negative allosteric modulator of PAR1. In addition to inhibiting human platelet aggregation, it showed superior anti-inflammatory activity to ML161 in a qPCR assay measuring the expression of tissue factor in response to the cytokine TNF-alpha in endothelial cells. Additionally, NRD-21 is much more plasma stable than ML161, and is a promising lead compound for the parmodulin class for anti-thrombotic and anti-inflammatory indications

TGF-β Inducible Early Gene 1 Regulates Osteoclast Differentiation and Survival by Mediating the NFATc1, AKT, and MEK/ERK Signaling Pathways

TGF-β Inducible Early Gene-1 (TIEG1) is a Krüppel-like transcription factor (KLF10) that was originally cloned from human osteoblasts as an early response gene to TGF-β treatment. As reported previously, TIEG1−/− mice have decreased cortical bone thickness and vertebral bone volume and have increased spacing between the trabeculae in the femoral head relative to wildtype controls. Here, we have investigated the role of TIEG1 in osteoclasts to further determine their potential role in mediating this phenotype. We have found that TIEG1−/− osteoclast precursors differentiated more slowly compared to wildtype precursors in vitro and high RANKL doses are able to overcome this defect. We also discovered that TIEG1−/− precursors exhibit defective RANKL-induced phosphorylation and accumulation of NFATc1 and the NFATc1 target gene DC-STAMP. Higher RANKL concentrations reversed defective NFATc1 signaling and restored differentiation. After differentiation, wildtype osteoclasts underwent apoptosis more quickly than TIEG1−/− osteoclasts. We observed increased AKT and MEK/ERK signaling pathway activation in TIEG1−/− osteoclasts, consistent with the roles of these kinases in promoting osteoclast survival. Adenoviral delivery of TIEG1 (AdTIEG1) to TIEG1−/− cells reversed the RANKL-induced NFATc1 signaling defect in TIEG1−/− precursors and eliminated the differentiation and apoptosis defects. Suppression of TIEG1 with siRNA in wildtype cells reduced differentiation and NFATc1 activation. Together, these data provide evidence that TIEG1 controls osteoclast differentiation by reducing NFATc1 pathway activation and reduces osteoclast survival by suppressing AKT and MEK/ERK signaling

3-D Ultrastructure of O. tauri: Electron Cryotomography of an Entire Eukaryotic Cell

The hallmark of eukaryotic cells is their segregation of key biological functions into discrete, membrane-bound organelles. Creating accurate models of their ultrastructural complexity has been difficult in part because of the limited resolution of light microscopy and the artifact-prone nature of conventional electron microscopy. Here we explored the potential of the emerging technology electron cryotomography to produce three-dimensional images of an entire eukaryotic cell in a near-native state. Ostreococcus tauri was chosen as the specimen because as a unicellular picoplankton with just one copy of each organelle, it is the smallest known eukaryote and was therefore likely to yield the highest resolution images. Whole cells were imaged at various stages of the cell cycle, yielding 3-D reconstructions of complete chloroplasts, mitochondria, endoplasmic reticula, Golgi bodies, peroxisomes, microtubules, and putative ribosome distributions in-situ. Surprisingly, the nucleus was seen to open long before mitosis, and while one microtubule (or two in some predivisional cells) was consistently present, no mitotic spindle was ever observed, prompting speculation that a single microtubule might be sufficient to segregate multiple chromosomes