Transcriptome and Metabolite Insights into Domestication Process of Cultivated Barley in China

The domestication process of cultivated barley in China remains under debate because of the controversial origins of barley. Here, we analyzed transcriptomic and non-targeted metabolic data from 29 accessions together with public resequencing data from 124 accessions to explore the domestication process of cultivated barley in China (Cb-C). These analyses revealed that both Cb-C and Tibetan wild barley (Wb-T) were the descendants of wild barley from the Near East Fertile Crescent (Wb-NE), yielding little support for a local origin of Wb-T. Wb-T was more likely an intermediate in the domestication process from Wb-NE to Cb-C. Wb-T contributed more genetically to Cb-C than Wb-NE, and was domesticated into Cb-C about 3300 years ago. These results together seem to support that Wb-T may be a feralized or hybrid form of cultivated barley from the Near East Fertile Crescent or central Asia. Additionally, the metabolite analysis revealed divergent metabolites of alkaloids and phenylpropanoids and these metabolites were specifically targeted for selection in the evolutionary stages from Wb-NE to Wb-T and from Wb-T to Cb-C. The key missense SNPs in the genes HORVU6Hr1G027650 and HORVU4Hr1G072150 might be responsible for the divergence of metabolites of alkaloids and phenylpropanoids during domestication. Our findings allow for a better understanding of the domestication process of cultivated barley in China.

Abstract 13347: Oxygen Metabolism After Cardiac Arrest: Patterns and Association With Survival

Introduction: How mitochondrial damage from cardiac arrest (CA) and resuscitation affects oxygen metabolism, and whether changes in metabolism are associated with outcome, is not well understood. We previously reported an association between higher oxygen consumption (VO2) in the first 12 hours after return of spontaneous circulation (ROSC) and survival in 17 post-arrest (PA) patients. The present study was conducted to investigate the association of VO2, VCO2 and RQ with survival in a larger PA cohort. Methods: From adult patients enrolled in several CA trials at our center, we selected those receiving targeted temperature management with ≥60 minutes of post-ROSC metabolic data collected in the first 24 hours after ROSC, using a gas exchange monitor that measures continuous VO2, VCO2 and RQ.The area under the curve (AUC) for VO2, VCO2 and RQ was calculated using all available values in the first 12 and 24 hours after ROSC. For both time periods, logistic regression was used to describe the relationship between survival and each AUC. We adjusted for temperature, sedation, and vasopressor s. Hourly medians were plotted by survival. Results: Of 64 patients included, 32 (50%) survived. There was no significant association between survival and AUC-VO2 or AUC-VCO2 in the first 12 (n=43) or 24 (n=64) hours after ROSC. 21 (49%) had a median RQ <0.7 in the first 12 hours, and there was an association between survival and AUC-RQ in this time period (see table). Conclusion: There were no significant associations between VO2, VCO2 and survival in the first 12 and 24 hours after ROSC. RQ was abnormally low in many patients, and higher RQ in the first 12 hours after ROSC was associated with survival.

BIOM-14. METABOLIC BIOMARKERS OF TERT-TARGETED THERAPY FOR HUMAN GLIOBLASTOMA DETECTED BY MAGNETIC RESONANCE SPECTROSCOPY

BACKGROUND TERT promoter mutations that result in TERT expression are observed in over 80% of GBM. Moreover, the upstream transcription factor GABPB1 was recently identified as an ideal therapeutic target for tumors with TERT promoter mutations. In that context, non-invasive reliable biomarkers that can help detect TERT expression are needed. The aim of this research was to assess the value of MRS-detectable metabolic changes as biomarkers of TERT expression and TERT-targeted therapy in GBM. METHODS Genetically engineered GBM cells (NHARas/TERT) treated with TERT siRNA were compared to siCtrl-treated cells, and stable TERT and GABPB1 knock down GBM cells (U251, GBM1) were compared to shCtrl. 1H-MRS and 13C-MRS metabolic data was acquired from cell extracts using a Bruker 500MHz scanner. Hyperpolarized MRS studies of live cells used a HyperSense DNP polarizer and data was acquired using a Varian 500MHz scanner. Spectra were analyzed using Mnova and Matlab software. Multivariate data analysis was performed using SIMCA software. RESULTS Unbiased PCA analysis of 1H-MRS metabolic data showed separation of TERT or GABPB1 knock down and control cells. VIP predictive scores revealed that lactate and GSH were the top altered metabolites with a significant drop observed in both metabolites in every model following TERT silencing. Consistent with the reduction in GSH, spectrophotometric assays showed a significant drop in NADPH and NADH. 2-13C glucose flux analysis revealed that both glycolysis and PPP-related metabolites were reduced in TERT knock down cells. Hyperpolarized [1-13C]-pyruvate flux to lactate was also reduced, confirming that the glycolytic pathway was altered following TERT knock down. CONCLUSION 1H MRS-detectable lactate and GSH, combined with hyperpolarized 13C MRS-detectable metabolic fluxes, could serve as metabolic biomarkers of TERT-targeted therapy for human GBM with TERT promoter mutations. These biomarkers could be translated to the clinical, improve the monitoring of GBM patients and advance precision medicine.

Metabolic Low-Frequency Oscillation and Abbreviated Protocol for Estimating REE by Indirect Calorimetry in Healthy Adults

Objectives: The aim of this study is to propose a new wave protocol to identify low-frequency oscillations for evaluating resting energy expenditure (REE) and compare its performance with the 5-minute interval abbreviated protocol and standard protocol. Research methods & procedures: Consecutive 20-minute indirect calorimetry (IC) was used to collect metabolic data from 23 women and 37 men (between 23 and 43 years old). Sliding window filter algorithms were used to eliminate noise. Three protocols were used to evaluate REE: averaging the data between two consecutive waves (wave protocol), averaging the second 5-minute intervals (interval protocol), and averaging the last 15-minute REE (standard protocol). Results: Based on 60 healthy participants' metabolic data, compared with the interval protocol, the wave protocol showed better consistency with the standard protocol. The mean bias (limits of agreement) using the wave protocol was 0.3458% (-7.817% to 8.509%), and that using the interval protocol was -1.720% (-16.06% to 12.62%). The time required to evaluate REE with the wave protocol and interval protocol was measured. The measurement time for the interval protocol was 10 minutes, while the average measurement time for the wave protocol was 9.75 minutes. Conclusions: We recommend the wave protocol for estimating REE in healthy people. This abbreviated protocol can identify low-frequency oscillations and consider individual differences to more accurately reflect the baseline REE compared to the interval protocol. Compared with the standard protocol, the measurement time of the wave protocol was reduced by nearly half (from 20 minutes (standard protocol) to 9.75 minutes).

CSF Metabolomics of Tuberculous Meningitis: A Review

From the World Health Organization’s global TB report for 2020, it is estimated that in 2019 at least 80,000 children (a particularly vulnerable population) developed tuberculous meningitis (TBM)—an invariably fatal disease if untreated—although this is likely an underestimate. As our latest technologies have evolved—with the unprecedented development of the various “omics” disciplines—a mountain of new data on infectious diseases have been created. However, our knowledge and understanding of infectious diseases are still trying to keep pace. Metabolites offer much biological information, but the insights they permit can be difficult to derive. This review summarizes current metabolomics studies on cerebrospinal fluid (CSF) from TBM cases and collates the metabolic data reported. Collectively, CSF metabolomics studies have identified five classes of metabolites that characterize TBM: amino acids, organic acids, nucleotides, carbohydrates, and “other”. Taken holistically, the information given in this review serves to promote the mechanistic action of hypothesis generation that will drive and direct future studies on TBM.

Objective method reveals minimum time required to quantify net cost of transport across slow to fast walking speeds

The U-shaped net cost of transport (COT) curve of walking has helped scientists understand the biomechanical basis that underlies energy minimization during walking. However, to produce an individual's net COT curve, data must be analyzed during periods of steady-rate metabolism. Traditionally, studies analyze the last few minutes of a 6-10 min trial, assuming that steady-rate metabolism has been achieved. Yet, it is possible that an individual achieves steady rates of metabolism much earlier. However, there is no consensus on how to objectively quantify steady-rate metabolism across a range of walking speeds. Therefore, we developed an objective method to determine the minimum time needed for humans to achieve steady rates of metabolism across slow to fast walking speeds. We hypothesized that a shorter time window could be used to produce a net COT curve that is comparable to the net COT curve created using traditional methods. We analyzed metabolic data from twenty-one subjects who completed several 7-min walking trials ranging from 0.50-2.00 m/s. We partitioned the metabolic data for each trial into moving 1-min, 2-min, and 3 min intervals and calculated their slopes. We statistically compared these slope values to values derived from the last 3-min of the 7-min trial, our 'gold' standard comparison. We found that a minimum of 2 min is required to achieve steady-rate metabolism and that data from 2-4 min yields a net COT curve that is not statistically different from the one derived from experimental protocols that are generally accepted in the field.

MO834LOWER POST DIALYSIS POTASSIUM AND HIGHER ULTRAFILTRATION RATE ARE INDEPENDENT PREDICTORS OF ALL-CAUSE MORTALITY IN DIALYSIS PATIENTS

Background and Aims Dialysis is a life-saving procedure for the end-stage kidney disease, but mortality in this category of patients is still high. The survival of these patients is much lower compared to the general population. Factors affecting this survival has been studied for years and still continue to be an important part of current studies. While ultrafiltration rate is known to be associated with mortality in prevalent dialysis patients an important predictor of survival is the control of potassium profile. The aim of our study was to assess the hemodynamic and biochemical data, and to identify any significant association between post-dialysis potassium and all-cause mortality. Method This is a prospective study of 308 patients on maintenance dialysis, followed for seven years, ending 2019. All patients are dialysis dependent for ESKD and getting treatment in a single-center. Hypokalemia was defined as a serum potassium level &lt; 3.5 mEq/L and high ultrafiltration rate (UFR) &gt; 13 ml/kg/h. Other hemodynamic and metabolic data were also evaluated The survival rate was analysed by Kaplan-Meier curves and Cox regression analysis. Results A total of 308 patients were enrolled in this study. Mean age was 52 ± 15.6 years; 62.3% of pts were male; BMI 24.7±4.2. Of these, 55 patients (17.9%) died during the follow-up period. Our data showed the presence left ventricular hypertrophy (p=0.010), peripheral artery disease (p&lt;0.0001), diastolic disfunction (p&lt;0.01) and ultrafiltration rate during dialysis &gt;13ml/kg/h (p=0.002) were the most important predictors of mortality. Metabolic abnormalities, low albumin (p&lt;0.0005), hyperphosphatemia (p=0.011), post-dialysis potassium (p=0.037) were significantly associated with higher mortality. Logistic regression analysis of the metabolic data identified post-dialysis potassium (OR 0.242, 95% CI 0.074 – 0.793, p=0.019), and logistic regression analysis of the hemodynamic data identified ultrafiltration ratio (OR 0.149, CI 0.033 – 0.673, p=0.013) as independent predictors of all-cause mortality. Conclusion Lower post dialysis potassium levels and higher ultrafiltration rate are independently associated with higher all-cause and CV mortality in prevalent hemodialysis patients. Therefore the potassium profile and the UFR of the dialysis patients needs close monitoring and optimal control. The individualization of the dialysis prescription is recommended for each patient and it has an important role in preventing the occurrence of complication with immediate and long term effects. Management of dialysis patients should focus especially on reducing the risk of hypokalemia, not only that of hyperkalemia.

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.