Hypoxia Increases Nitric Oxide-Dependent Inhibition of Angiogenic Growth

Nitric oxide (NO) is a proangiogenic factor acting through the soluble guanylate cyclase (sGC) pathway. However, angiogenic growth increases energy demand, which may be hampered by NO inhibition of cytochrome c oxidase (CcO). Then, NO activity would be the balanced result of sGC activation (pro-angiogenic) and CcO inhibition (anti-angiogenic). NO activity in a rat and eNOS−/− mice aortic ring angiogenic model and in a tube formation assay (human aortic endothelial cells) were analyzed in parallel with mitochondrial O2 consumption. Studies were performed with NO donor (DETA-NO), sGC inhibitor (ODQ), and NOS or nNOS inhibitors (L-NAME or SMTC, respectively). Experiments were performed under different O2 concentrations (0–21%). Key findings were: (i) eNOS-derived NO inhibits angiogenic growth by a mechanism independent on sGC pathway and related to inhibition of mitochondrial O2 consumption; (ii) NO inhibition of the angiogenic growth is more evident in hypoxic vessels; (iii) in the absence of eNOS-derived NO, the modulation of angiogenic growth, related to hypoxia, disappears. Therefore, NO, but not lower O2 levels, decreases the angiogenic response in hypoxia through competitive inhibition of CcO. This anti-angiogenic activity could be a promising target to impair pathological angiogenesis in hypoxic conditions, as it occurs in tumors or ischemic diseases.

Korean Red Ginseng Improves Astrocytic Mitochondrial Function by Upregulating HO-1-Mediated AMPKα–PGC-1α–ERRα Circuit after Traumatic Brain Injury

Heme oxygenase-1 (HO-1) exerts beneficial effects, including angiogenesis and energy metabolism via the peroxisome proliferator-activating receptor-γ coactivator-1α (PGC-1α)–estrogen-related receptor α (ERRα) pathway in astrocytes. However, the role of Korean red ginseng extract (KRGE) in HO-1-mediated mitochondrial function in traumatic brain injury (TBI) is not well-elucidated. We found that HO-1 was upregulated in astrocytes located in peri-injured brain regions after a TBI, following exposure to KRGE. Experiments with pharmacological inhibitors and target-specific siRNAs revealed that HO-1 levels highly correlated with increased AMP-activated protein kinase α (AMPKα) activation, which led to the PGC-1α-ERRα axis-induced increases in mitochondrial functions (detected based on expression of cytochrome c oxidase subunit 2 (MTCO2) and cytochrome c as well as O2 consumption and ATP production). Knockdown of ERRα significantly reduced the p-AMPKα/AMPKα ratio and PGC-1α expression, leading to AMPKα–PGC-1α–ERRα circuit formation. Inactivation of HO by injecting the HO inhibitor Sn(IV) protoporphyrin IX dichloride diminished the expression of p-AMPKα, PGC-1α, ERRα, MTCO2, and cytochrome c in the KRGE-administered peri-injured region of a brain subjected to TBI. These data suggest that KRGE enhanced astrocytic mitochondrial function via a HO-1-mediated AMPKα–PGC-1α–ERRα circuit and consequent oxidative phosphorylation, O2 consumption, and ATP production. This circuit may play an important role in repairing neurovascular function after TBI in the peri-injured region by stimulating astrocytic mitochondrial biogenesis.

PSIX-10 Effect of different levels of DMI on O2 consumed, CO2 released, growth and carcass characteristic in feedlot cattle

Our objective was to determine the effect of different levels of dry matter intake (DMI) on O2 consumption, CO2 emission, growth, and carcass characteristic in feedlot cattle. The experiment used 60 individually fed backgrounded Angus × SimAngus-crossbred steers (n = 30) in a randomized complete block design. Steers (paired blocked by body weight and gain to feed ratio (G:F) were randomly allocated to one of the following treatments: ad-libitum (AI) or restricted intake (RI; the same diet fed at 85% of the AI) finishing diet. The diet contained 61% cracked corn, 9% corn silage, 15% DDGS, 5% soyhulls, and 10% of a protein-mineral-vitamin premix. Measurement of CO2 emission, and consumption of O2, were taken using the Greenfeed system (n = 15/treatment) once the steers were fed for 140 days. Plasma and gas samples were collected 10 d before slaughter, 1 h before and 2 h after feeding. Plasma glucose and insulin concentration and gasses (O2 and CO2) were analyzed using the MIXED procedure of SAS evaluating the fixed effect of treatment, time (repeated measurement) and their interaction. Growth and carcass characteristics were analyzed with a similar model, without the time statement and its interaction. Compared with RI, AI steers had greater (P < 0.01) DMI and average daily gain (ADG). Steers on AI tended to have greater final body-weight (BW) (P = 0.07) and ribeye area (P = 0.09) (Table 1). There was no effect of treatment (P ≥ 0.11) on G:F, subcutaneous (BF) and intramuscular (IM) fat, O2 consumption and CO2 emission. Plasma glucose concentration of AI steers were greater before and after feeding than RI (P < 0.05; Table 2). In conclusion, feeding steers ad-libitum increased DMI, ADG, and plasma glucose concentration, but does not affect G:F, BF, IM fat, CO2 emission, and consumption of O2.

PSXIII-29 Late-Breaking: Relationships between feed efficiency traits, gas emissions, and calculated heat production and respiratory quotients in mature beef cows

This research evaluated relationships between feed efficiency measurements including residual feed intake (RFI), gain to feed ratio (G:F) and enteric gas production of methane (CH4), carbon dioxide (CO2), oxygen, (O2) along with calculated respiratory quotient (RQ) and heat production (HP) of mature beef cows. Sixty pregnant multiparous Angus beef cows were measured for individual intake for 10 weeks leading up to parturition. Cows were individually fed using the Insentec feeding system to measure individual feed intake. Pens of cows were rotated weekly to the C-Lock Greenfeed trailer (C-Lock Inc.) to measure gas production for a minimum 2 periods per group. Cows were weighed, body condition scored, and ultrasound performed for rib and rump fat depth every 28 days and calf birth weight was recorded. Pearson correlations were conducted to determine relationships between gas exchange measurements and feed efficiency measures. The model for RFI included ADG, pregnancy corrected BW, and ultrasound measures of rib and rump fat (R2= 0.32). Methane emissions were 263±40.3 g CH4/d in dry lot, and daily enteric CH4 emissions per kg of pregnancy corrected body weight ranging from 0.26 to 0.53 g CH4/kg pcBW. Pearson correlations found positive correlations between DMI and CH4 and CO2 (P ≤ 0.001). Pregnancy corrected BW was also positively correlated with total O2 consumption and calculated HP (P ≤ 0.001) and negatively correlated with RQ (P = 0.04). However, RFI and feed conversion ratio were not significantly correlated with gas measurements, or calculated HP or RQ (P ≥ 0.1), although DMI/kg of BW tended (P = 0.098) to be negatively correlated with O2 consumption. In conclusion, accurately identifying feed efficiency in mature beef cows continues to be a challenge. However, the inclusion of gas exchange measures and calorimetry measurements may be able to better assess metabolic efficiency in mature beef cattle.

Investigation of intra-day variability of gaseous measurements in sheep using Portable accumulation chambers 1

Portable accumulation chambers (PAC) enable short term spot measurements of gaseous emissions including methane (CH4), carbon dioxide (CO2) and oxygen (O2) consumption from small ruminants. To date the differences in morning and evening gaseous measurements in the PAC have not been investigated. The objectives of this study were to investigate: 1) the optimal measurement time in the PAC, 2) the appropriate method of accounting for the animal’s size when calculating the animal’s gaseous output, and 3) the intra-day variability of gaseous measurements. A total of 12 ewe lambs (c. 10 to 11 months of age) were randomly selected each day from a cohort of 48 animals over nine consecutive days. Methane emissions from the 12 lambs were measured in 12 PAC during two measurement runs daily, AM (8 to 10 h) and PM (14 to 16 h). Animals were removed from Perennial ryegrass silage for at least 1 hour prior to measurements in the PAC and animals were assigned randomly to each of the 12 chambers. Methane (ppm) concentration, O2 and CO2 percentage were measured at 5 time points (T1 = 0.0 min, T2 = 12.5 min, T3 = 25.0 min, T4 = 37.5 min and T5 = 50.0 min from entry of the first animal into the first chamber) using an Eagle 2 monitor. The correlation between time points T5-T1 (i.e. 50 min minus 0 min after entry of the animal to the chamber) and T4-T1 was 0.95, 0.92 and 0.77 for CH4, O2 and CO2, respectively (P<0.01). The correlation between CH4 and CO2 output and O2 consumption, calculated with live-weight and with body volume was 0.99 (P<0.001). The correlation between the PAC measurement recorded on the same animal in the AM and PM measurement runs was 0.73. Factors associated with CH4 production included: day and time of measurement, the live-weight of the animal and the hourly relative humidity. Results from this study suggest that the optimal time for measuring an animal’s gaseous output in the PAC is 50 min, that live-weight should be used in the calculation of gaseous output from an animal and that the measurement of an animal’s gaseous emissions in either the AM or PM does not impact on the ranking of animals when gaseous emissions are measured using the feeding and measurement protocol outlined in the present study.

Predicting Heavy Oil Combustion Kinetics with Machine Learning

Heavy oil resources are becoming increasingly important for the global oil supply, and consequently there has been renewed interest in techniques for extracting heavy oil. Among these, in-situ combustion (ISC) has tremendous potential for late-stage heavy oil fields, as well as high viscosity, very deep, or other unconventional reservoirs. A critical step in evaluating the use of ISC in a potential project is developing an accurate chemical reaction model to employ for larger-scale simulations. Such models can be difficult to calibrate, however, that in turn can lead to large errors in upscaled simulations. Data-driven models of ISC kinetics overcome these issues by foregoing the calibration step and predicting kinetics directly from laboratory data. In this work, we introduce the Non-Arrhenius Machine Learning Approach (NAMLA). NAMLA is a machine learning-based method for predicting O2 consumption in heavy oil combustion directly from ramped temperature oxidation (RTO) experimental data. Our model treats the O2 consumption as a function of only temperature and total O2 conversion and uses a locally-weighted linear regression model to predict the conversion rate at a query point. We apply this method to simulated and experimental data from heavy oil samples and compare its ability to predict O2 consumption curves with a previously proposed interpolation-based method. Results show that the presented method has better performance than previously proposed interpolation models when the available experimental data is very sparse or the query point lies outside the range of RTO experiments in the dataset. When available data is sufficiently dense or the query point is within the range of RTO curves in the training set, then linear interpolation has comparable or better accuracy than the proposed method. The biggest advantage of the proposed method is that it is able to compute confidence intervals for experimentally measured or estimated O2 consumption curves. We believe that future methods will be able to use the efficiency and accuracy of interpolation-based methods with the statistical properties of the proposed machine learning approach to better characterize and predict heavy oil combustion.