Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development

Abstract Background Rice (Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked. Results In this study, we performed RNA-Seq and characterized the expression profiles of differentiated tissues from Oryza sativa Zhonghua 11, including leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and embryo, endosperm, lemma and palea of the mature grain stage. By integrating this set of transcriptome data of different rice tissues at different stages with a genome-scale rice metabolic model, we generated tissue-specific models and investigated the shift of metabolic patterns, and the discrepancy between transcriptomic and metabolic level. We found although the flux patterns are not very similar with the gene expression pattern, the tissues at booting stage and mature grain stage can be separately clustered by primary metabolism at either level. While the gene expression and flux distribution of secondary metabolism is more diverse across tissues and stages. The critical rate-limiting reactions and pathways were also identified. In addition, we compared the patterns of the same tissue at different stages and the different tissues at same stage. There are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype, and might largely because of the complex post-transcriptional modification. Conclusions The tissue-specific models revealed more detail metabolic pattern shift among different tissues and stages, which is of great significance to uncover mechanism of rice grain development and further improve production and quality of rice

A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers

Additional file 3: of Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development

Metabolic flux distribution of primary metabolism in different tissues at booting and mature grain stages. (ZIP 3206 kb

Additional file 2: of Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development

Input data for iMAT to construct tissue-specific models, including rice model, expression data, and transformation of reaction activity. (TIFF 3788 kb

Intercostal Cryo Nerve Block in Minimally Invasive Cardiac Surgery: The Prospective Randomized FROST Trial

Abstract Introduction Intercostal cryo nerve block has been shown to enhance pulmonary function recovery and pain management in post-thoracotomy procedures. However, its benefit have never been demonstrated in minimal invasive thoracotomy heart valve surgery (Mini-HVS). The purpose of the study was to determine whether intraoperative intercostal cryo nerve block in conjunction with standard of care (collectively referred to hereafter as CryoNB) provided superior analgesic efficacy in patients undergoing Mini-HVS compared to standard-of-care (SOC). Methods FROST was a prospective, 3:1 randomized (CryoNB vs. SOC), multicenter trial in patients undergoing Mini-HVS. The primary endpoint was the 48-h postoperative forced expiratory volume in 1 s (FEV1) result. Secondary endpoints were visual analog scale (VAS) scores for pain at the surgical site and general pain, intensive care unit and hospital length-of-stay, total opioid consumption, and allodynia at 6 months postoperatively. Results A total of 84 patients were randomized to the two arms of the trial CryoNB (n = 65) and SOC (n = 19). Baseline Society of Thoracic Surgeons Predictive Risk of Mortality (STS PROM) score, ejection fraction, and FEV1 were similar between cohorts. A higher 48-h postoperative FEV1 result was demonstrated in the CryoNB cohort versus the SOC cohort (1.20 ± 0.46 vs. 0.93 ± 0.43 L; P = 0.02, one-sided two-sample t test). Surgical site VAS scores were similar between the CryoNB and SOC cohorts at all postoperative timepoints evaluated, but VAS scores not related to the surgical site were lower in the SOC group at 72, 94, and 120 h postoperatively. The SOC cohort had a 13% higher opioid consumption than the CryoNB cohort. One of 64 CryoNB patients reported allodynia that did not require pain medication at 10 months. Conclusions The results of FROST demonstrated that intercostal CryoNB provided enhanced FEV1 score at 48 h postoperatively with optimized analgesic effectiveness versus SOC. Future larger prospective randomized trials are warranted to determine whether intercostal CryoNB has an opioid-sparing effect in patients undergoing Mini-HVS. Trial Registration Clinicaltrials.gov identifier: NCT02922153.http://deepblue.lib.umich.edu/bitstream/2027.42/173960/1/40122_2021_Article_318.pd

Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development

Abstract Background Rice (Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked. Results In this study, we performed RNA-Seq and characterized the expression profiles of differentiated tissues from Oryza sativa Zhonghua 11, including leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and embryo, endosperm, lemma and palea of the mature grain stage. By integrating this set of transcriptome data of different rice tissues at different stages with a genome-scale rice metabolic model, we generated tissue-specific models and investigated the shift of metabolic patterns, and the discrepancy between transcriptomic and metabolic level. We found although the flux patterns are not very similar with the gene expression pattern, the tissues at booting stage and mature grain stage can be separately clustered by primary metabolism at either level. While the gene expression and flux distribution of secondary metabolism is more diverse across tissues and stages. The critical rate-limiting reactions and pathways were also identified. In addition, we compared the patterns of the same tissue at different stages and the different tissues at same stage. There are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype, and might largely because of the complex post-transcriptional modification. Conclusions The tissue-specific models revealed more detail metabolic pattern shift among different tissues and stages, which is of great significance to uncover mechanism of rice grain development and further improve production and quality of rice